Document

QUANTCONNECT LOCAL PLATFORM

Guide through creating a project, running your first backtest, and live algo trading in QuantConnect Local Platform.

The Local Platform enables you to seamlessly develop quant strategies on-premise and in QuantConnect Cloud, getting the best of both environments. With Local Platform, you can harness your local version control, autocomplete, and coding tools with the full power of a scalable cloud at your finger tips. We intend to keep complete feature parity with our cloud environment, allowing you to harness cloud or local datasets to power on-premise quantitative research.

We encourage a hybrid “cloud + local” workflow, so you can use right tool for each stage of your development process. With the Local Platform, you can create, debug, and run projects on premise while using your own on-site tools. With the Cloud Platform you can deploy backtests at scale and harness our massive data library at low cost.

Follow these steps to create a new trading algorithm and backtest it in QuantConnect Cloud:

1. Install Local Platform .

2. Open Visual Studio Code.

3. In the Initialization Checklist panel, click Login to QuantConnect .

4. In the Visual Studio Code window, click Open .

5. On the Code Extension Login page, click Grant Access .

6. In VS Code, in the Select Workspace panel, click Pull Organization Workspace .

7. In the Pull QuantConnect Organization Workspace window, click the cloud workspace ( organization ) that you want to pull.

8. In the Pull QuantConnect Organization Workspace window, create a directory to serve as the organization workspace and then click Select .

If you are running Docker on Windows using the legacy Hyper-V backend instead of the new WSL 2 backend, you need to enable file sharing for your temporary directories and for your organization workspace. To do so, open your Docker settings, go to Resources > File Sharing and add C: / Users / / AppData / Local / Temp and your organization workspace path to the list. Click Apply & Restart after making the required changes.

9. In the Open Project panel, click Create Project .

10. Enter the project name and then press Enter .

Congratulations! You just created your first local project.

11. In the top-right corner of VS Code, click Build and then click Backtest .

The backtest results page displays your algorithm’s performance over the backtest period.

Introduction

There are 5 tiers of organizations and each tier has its own set of features on Local Platform. To accommodate the growth of your trading skills and business, you can adjust the tier of your organization at any time.

Hybrid Workflow

The Local Platform lets you run backtests, deploy research notebooks, and deploy live algorithms on your local machine and in QuantConnect Cloud. This gives you the best of both works where you can utilize your local hardware or our scalable cloud compute systems.

Version Control

The Local Platform syncs your local and cloud project files. If you pull your cloud projects to your local machine, you can use your own version control systems to track project changes.

Self-Sovereign Security

The Local Platform offers you the ability to take ownership of your project security. On the Institution tier, you can create local projects without pushing them to QuantConnect Cloud.

Custom LEAN Images

With the Local Platform, you can use custom images of the LEAN to add extensions or fixes at your convenience, and then run these images with the Local Platform interface.

On-Premise Compute

Out of the box, the Local Platform enables you to run backtests, optimizations, research notebooks, and deploy live algorithms on a local computer or single server.

Private Cloud

Build your own private cloud cluster on private hardware or clouds. The Private Cloud add-on allows scaling the Local Platform to serve multiple engineers on thin clients, like laptops, with powerful work servers that can be loaded with GPUs and centralized copies of proprietary data.

This is a much more efficient local-cloud setup and can easily scale to large teams.

Coding

Backtesting & Optimization

The following table shows the backtesting and optimization features of each platform:

Live Trading

Introduction

The deployment target setting allows you to switch modes from local to cloud platforms, choosing where you run your algorithm. Local Platform targets are denoted with blue icons and Cloud Platform targets are denoted with gold icons.

Local

The Local Platform deployment target is your local machine. Follow these steps to set the deployment target of a project to Local Platform:

After you set the deployment target to Local Platform, the following icons are blue:

Cloud

Feature	Platform
Development Environment _{The tool you can use to edit project files}	Any IDE	Cloud-hosted VS Code
Version Control _{Track file changes over time and easily revert mistakes}	Your own git systems	Access historical project files through your backtest results
Anonymous Projects _{Create and edit local projects without syncing to QuantConnect Cloud}	Self-sovereign security	Managed by QuantConnect
Custom LEAN Versions _{Build and run custom versions of LEAN}
Autocomplete _{Easy-to-use tool to speed up your development}

Feature	Platform
Data Source _{Where does the data come from?}	Licensed data	Provided by QuantConnect
Data Maintenance _{Who ensures the data is clean and ready?}	Self-maintaned	QuantConnect Team
Compute _{Where is the hardware that runs backtests?}	Your compute	QuantConnect Cloud compute
Proprietary Data _{Data that's not in the Dataset Market}	Never leaves premise	Upload to cloud
Debugging _{Easy-to-use tool for solving coding errors}

Feature	Platform
Data Source _{Where does the data come from?}	Licensed data	Provided by QuantConnect
Stability _{How stable is your live trading environment?}	Your local setup	Stable co-located environment
Notifications _{SMS, email, Telegram, and webhooks}

Icon	Name
	Build
	Backtest
	Debug
	Optimize
	Live Trading
	Backtest Results

The Cloud Platform deployment target is a collection of servers that the QuantConnect team manages. It's the same deployment target you use if you create projects, spin up research nodes, and deploy algorithms on the QuantConnect website. For more information about QuantConnect Cloud, including our infrastructure and usage quotas, see Cloud Platform .

After you set the deployment target to Cloud Platform, the following icons are gold:

Private Cloud

The Private Cloud add-on to the Local Platform enables a cluster of centrally managed servers to run work tasks from a distributed team of users. This is much more efficient than each quant having a full copy of data or their own GPU resources. A single work server can be loaded with GPUs and a centralized copy of your company's proprietary data.

Comparison

Note the following differences between the Local Platform and Cloud Platform deployment targets.

Data

Compute

Icon	Name
	Build
	Backtest
	Optimize
	Live Trading
	Backtest Results

The Local Platform target uses your on-premise hardware. The Cloud Platform target uses the QuantConnect Cloud compute. For more information about the backtesting, research, and live trading nodes in QuantConnect Cloud, see Resources .

Management

The Local Platform target is under the management of your on-premise team. The Cloud Platform target is under the management of the QuantConnect team.

Hardware Procurement

The Local Platform target uses your on-premise hardware, so it requires you to procure and management your own hardware. The Cloud Platform target uses the hardware in QuantConnect Cloud, so you don't need to procure or manage any of the hardware.

Introduction

It takes 10 minutes to install Local Platform and about 1 hour to download the latest LEAN image. The Local Platform requires Docker. When you launch Local Platform, we scan for Docker and prompt you to install it to continue. We run all algorithms in a Docker container to avoid installing any dependencies on your computer.

Requirements

Windows systems must meet the following minimum requirements to run Local Platform:

You need an internet connection for things like downloading updates, collaborating with team members, and syncing your projects with QuantConnect Cloud. Trading Firm and Institution organizations can run local backtests and research notebooks without an internet connection for up to 24 hours.

Install Docker

If you run the LEAN engine locally with QuantConnect Local Platform, LEAN executes in a Docker container. These Docker containers contain a minimal Linux-based operating system, the LEAN engine, and all the packages available to you on QuantConnect.com. It is therefore required to install Docker if you plan on using QuantConnect Local Platform to run the LEAN engine locally.

By default, Docker doesn't automatically start when your computer starts. So, when you run the LEAN engine with QuantConnect Local Platform for the first time after starting your computer, you must manually start Docker. To automatically start Docker, open the Docker Desktop application, click , and then enable the Start Docker Desktop when you log in check box.

Install Local Platform

If you open Visual Studio Code and it asks you to log in to QuantConnect, you successfully installed Local Platform.

Next Steps

Troubleshooting

The following sections explain how to solve some issues you may encounter while installing Local Platform.

Docker with WSL 2 Features

When you download Docker Desktop, you need to select the Enable WSL 2 Features check box. After you install Docker and restart your computer, if Docker prompts you that the WSL 2 installation is incomplete, follow the instructions in the dialog shown by Docker to finish the WSL 2 installation.

Windows Security

If you can't synchonize your workpace, follow these steps to configure controlled folder access on your computer:

Docker Not Found

If you have Docker installed but the Local Platform can't detect it, update your Executable Path: Docker setting to be the path to your Docker executable.

LEAN CLI Account Syncronization

Local Platform and the LEAN CLI share your login credentials. If you log in to your account on Local Platform or the LEAN CLI, you log into that account for both Local Platform and the LEAN CLI.

Further Support

Introduction

Requirements

Install Docker

Install Local Platform

If you open Visual Studio Code and it asks you to log in to QuantConnect, you successfully installed Local Platform.

Next Steps

Troubleshooting

The following sections explain how to solve some issues you may encounter while installing Local Platform.

Docker Not Found

If you have Docker installed but the Local Platform can't detect it, update your Executable Path: Docker setting to be the path to your Docker executable.

LEAN CLI Account Syncronization

Local Platform and the LEAN CLI share your login credentials. If you log in to your account on Local Platform or the LEAN CLI, you log into that account for both Local Platform and the LEAN CLI.

Further Support

Introduction

Requirements

Linux systems must meet the following minimum requirements to run Local Platform:

Install Docker

Install Local Platform

If you open Visual Studio Code and it asks you to log in to QuantConnect, you successfully installed Local Platform.

Next Steps

Troubleshooting

The following sections explain how to solve some issues you may encounter while installing Local Platform.

Docker Not Found

If you have Docker installed but the Local Platform can't detect it, update your Executable Path: Docker setting to be the path to your Docker executable.

LEAN CLI Account Syncronization

Local Platform and the LEAN CLI share your login credentials. If you log in to your account on Local Platform or the LEAN CLI, you log into that account for both Local Platform and the LEAN CLI.

Further Support

Introduction

To use Local Platform, you need to grant it access to your QuantConnect account.

Log Out

Troubleshooting

Local Platform and the LEAN CLI share your login credentials. If you log in to your account on Local Platform or the LEAN CLI, you log into that account for both Local Platform and the LEAN CLI.

Introduction

An organization workspace is a directory that contains a data directory, a Lean configuration file, and all your project files from one of your organizations. You can have a separate organization workspace directory for each organization you're a member of on QuantConnect. These directories need a data directory and a Lean configuration file in order to run the LEAN engine on your local machine.

Pull Cloud Organization Workspaces

Follow these steps to pull one of your cloud organization workspaces and set it as your local organization workspace:

It takes a few minutes to create a new organization workspace directory and populate it with the the initial file structure . After the organization workspace is populated with the initial file structure, it pulls your cloud project files .

If you are running Docker on Windows using the legacy Hyper-V backend instead of the new WSL 2 backend, you need to enable file sharing for your temporary directories and for your organization workspace. To do so, open your Docker settings, go to and add C: / Users / <username> / AppData / Local / Temp and your organization workspace path to the list. Click Apply & Restart after making the required changes.

Change Organization Workspaces

Directory Structure

File/Directory	Description
data /	This directory contains the local data that LEAN uses to run locally. This directory is comes with sample data from the QuantConnect/Lean repository . As you download additional data from the dataset market, it's stored in this directory. Each organization workspace has its own data directory because each organization has its own data licenses.
storage /	This directory contains the Object Store data that LEAN uses to run locally.
lean.json	This file contains the Lean configuration that is used when running the LEAN engine locally. The configuration is stored as JSON with support for both single-line and multiline comments. The Lean configuration file is based on the Launcher/config.json file from the Lean repository. When you create a new organization workspace, the latest version of this file is downloaded and stored on your local drive.

As you add projects , the project files are added to your organization workspace directory. If you create and use shared libraries in your projects, the library files are added to a Library directory in your organization workspace.

Introduction

The Local Platform is configured by extension settings in VS Code and by the LEAN Engine settings. Change these settings at any time to suit your needs.

Extension Settings

LEAN Settings

Name	Description
Executable Path: Docker	A path to the Docker installation you want to use.
Executable Path: Lean	A path to the LEAN CLI executable you want to use.
Lean: Init	A path to the current organization workspace.
Sync: Local And Cloud Projects	Yes to synchronize cloud and local projects. Otherwise, No . No is only available for Institution organizations.
User: Preferred Language	The programming language to use when creating new projects. Py for Python or C# for C#.

The Lean configuration contains settings for locally running the LEAN engine. This configuration is created in the lean.json file when you pull or create an organization workspace . The configuration is stored as JSON, with support for both single-line and multiline comments.

The Lean configuration file is based on the Launcher / config.json file from the Lean GitHub repository. When you pull or create an organization workspace, the latest version of this file is downloaded and stored in your organization workspace. Before the file is stored, some properties are automatically removed because the Local Platform automatically sets them.

The Local Platform can update most of the values of the lean.json file. The following table shows the configuration settings that you need to manually adjust in the lean.json file if you want to change their values:

Introduction

Intellisense is a GUI tool in your code files that shows auto-completion options and presents the members that are accessible from the current object. The tool works by searching for the statement that you're typing, given the context. You can use Intellisense to auto-complete method names and object attributes. When you use it, a pop-up displays in the IDE with the following information:

Use Intellisense to speed up your algorithm development. It works with all of the default class members in Lean, but it doesn't currently support class names or user-defined objects.

Install Python Stubs

Before you use autocomplete, you may need to follow these steps to get the latest Python stubs:

Use Autocomplete

Introduction

Project collaboration is a real-time coding experience with other members of your team. Collaborating can speed up your development time. By working with other members in an organization, members within the organization can specialize in different parts of the project. On Local Platform, you can collaborate with your remote team members.

Video Demo

When there are multiple people working on the same project, the cursor of each member is visible in the IDE and all file changes occur in real-time for everyone. The following video demonstrates the collaboration feature:

Add Team Members

Name	Description	Default
`show-missing-data-logs`	Log missing data files. This is useful for debugging.	true
`algorithm-manager-time-loop-maximum`	The maximum amount of time the algorithm can spend in a single time loop .	20
`maximum-warmup-history-days-look-back`	The maximum number of days of data the history provider will provide during warm-up in live trading. The history provider expects older data to be on disk.	5
`maximum-chart-series`	The maximum number of chart series you can create in backtests.	30
`maximum-data-points-per-chart-series`	The maximum number of data points you can add to a chart series in backtests.	1,000,000

If the project has a shared library , the collaborator can access the project, but not the library. To grant them access to the library, add them as a collaborator to the library project.

Collaborator Quotas

The number of members you can add to a project depends on your organization's tier . The following table shows the number of collaborators each tier can have per project:

Toggle Live Control

You need to have added a member to the project to toggle their live control of the project.

Remove Team Members

Tier	Collaborators per Project
Free	Unsupported
Quant Researcher	Unsupported
Team	10
Trading Firm	Unlimited
Institution	Unlimited

To remove yourself as a collaborator from a project you don't own, delete the project .

Introduction

The latest master branch on the LEAN GitHub repository is the default engine branch that runs backtests, research notebooks, and live trading algorithms. The latest version of LEAN is generally the safest as it includes all bug fixes.

Change Branches

Follow these steps to change the LEAN engine branch that runs your backtests and live trading algorithms:

Changing the Lean engine branch only affects the current project. If you create a new project , the new project will use the master branch by default.

Custom Branches

Introduction

Unless you are working on an anonymous project, Local Platform automatically syncs your local project files with QuantConnect Cloud. Every time you save a file, Local Platform saves the changes in your local project and in the cloud version of the project.

Anonymous Projects

Anonymous projects are projects that are on your local machine and not synced with QuantConnect Cloud. These types of projects are only available for members in Institution organizations. Anonymous projects provide organizations the opportunity to take ownership of their projects security.

Supported File Types

When you save your local projects and push them to QuantConnect Cloud, it only pushes the Python, C#, and notebook files in your project. Projects can contain many other file types like json , csv , and html , but Local Platform only pushes your py , cs , and ipynb files.

Introduction

The Resources panel shows all of the backtest, research, and live trading nodes that Local Platform can use or is already using.

View Resources

To view the Resources panel, open a project and then, in the left navigation menu, click the

QuantConnect icon. The Resources panel is at the bottom of the Project panel.

Stop Nodes

To stop a node, open the Resources panel and then click the stop button next to the node.

Introduction

Libraries (or packages) are third-party software that you can use in your projects. You can use many of the available open-source libraries to complement the classes and methods that you create. Libraries reduce your development time because it's faster to use a pre-built, open-source library than to write the functionality. Libraries can be used in backtesting, research, and live trading. The environments support various libraries for machine learning, plotting, and data processing. As members often request new libraries, we frequently add new libraries to the underlying docker image that runs the Lean engine.

This feature is primarily for Python algorithms as not all Python libraries are compatible with each other. We've bundled together different sets of libraries into distinct environments. To use the libraries of an environment, set the environment in your project and add the relevant


    using


    import

statement of a library at the top of your file.

Set Environment

Default Environment

absl-py                                 2.1.0
accelerate                              0.30.1
adagio                                  0.2.4
aesara                                  2.9.3
aiohttp                                 3.9.5
aiosignal                               1.3.1
aiosqlite                               0.20.0
alembic                                 1.13.1
alibi-detect                            0.12.0
alphalens-reloaded                      0.4.3
altair                                  5.2.0
aniso8601                               9.0.1
annotated-types                         0.7.0
antlr4-python3-runtime                  4.9.3
anyio                                   4.4.0
appdirs                                 1.4.4
apricot-select                          0.6.1
arch                                    6.3.0
argon2-cffi                             23.1.0
argon2-cffi-bindings                    21.2.0
arrow                                   1.3.0
arviz                                   0.18.0
astropy                                 6.0.0
astropy-iers-data                       0.2024.6.3.0.31.14
asttokens                               2.4.1
astunparse                              1.6.3
async-lru                               2.0.4
attrs                                   23.2.0
Authlib                                 1.3.0
autograd                                1.6.2
autograd-gamma                          0.5.0
autokeras                               2.0.0
autoray                                 0.6.12
ax-platform                             0.3.7
Babel                                   2.15.0
bayesian-optimization                   1.4.3
beautifulsoup4                          4.12.3
bleach                                  6.1.0
blinker                                 1.8.2
blis                                    0.7.11
blosc2                                  2.6.2
bokeh                                   3.3.4
botorch                                 0.10.0
Bottleneck                              1.3.8
cachetools                              5.3.3
captum                                  0.7.0
catalogue                               2.0.10
catboost                                1.2.3
category-encoders                       2.6.3
causal-conv1d                           1.2.0.post2
chardet                                 5.2.0
check-shapes                            1.1.1
clarabel                                0.9.0
click                                   8.1.7
clikit                                  0.6.2
cloudpathlib                            0.16.0
cloudpickle                             3.0.0
cmdstanpy                               1.2.1
colorama                                0.4.6
colorcet                                3.1.0
colorlog                                6.8.2
colorlover                              0.3.0
colour                                  0.1.5
comm                                    0.2.2
confection                              0.1.5
cons                                    0.4.6
contourpy                               1.2.0
control                                 0.9.4
copulae                                 0.7.9
copulas                                 0.10.1
coreforecast                            0.0.9
cramjam                                 2.8.3
crashtest                               0.3.1
creme                                   0.6.1
cufflinks                               0.17.3
cvxopt                                  1.3.2
cvxpy                                   1.4.2
cycler                                  0.12.1
cymem                                   2.0.8
Cython                                  3.0.10
darts                                   0.28.0
dash                                    2.17.0
dash-core-components                    2.0.0
dash-cytoscape                          1.0.1
dash-html-components                    2.0.0
dash-table                              5.0.0
dask                                    2024.3.1
dask-expr                               1.0.5
dataclasses-json                        0.6.6
datasets                                2.17.1
deap                                    1.4.1
debugpy                                 1.6.7.post1
decorator                               5.1.1
deepmerge                               1.1.1
defusedxml                              0.7.1
Deprecated                              1.2.14
deprecation                             2.1.0
dgl                                     2.1.0
dill                                    0.3.8
dimod                                   0.12.14
dirtyjson                               1.0.8
diskcache                               5.6.3
distributed                             2024.3.1
dm-tree                                 0.1.8
docker                                  7.1.0
docutils                                0.20.1
DoubleML                                0.7.1
dropstackframe                          0.1.0
dtreeviz                                2.2.2
dtw-python                              1.3.1
dwave-cloud-client                      0.11.3
dwave-drivers                           0.4.4
dwave-greedy                            0.3.0
dwave-hybrid                            0.6.11
dwave-inspector                         0.4.4
dwave-inspectorapp                      0.3.1
dwave-neal                              0.6.0
dwave-networkx                          0.8.14
dwave-ocean-sdk                         6.9.0
dwave-preprocessing                     0.6.5
dwave-samplers                          1.2.0
dwave-system                            1.23.0
dwave-tabu                              0.5.0
dwavebinarycsp                          0.3.0
ecos                                    2.0.13
einops                                  0.7.0
EMD-signal                              1.6.0
empyrical-reloaded                      0.5.10
en-core-web-md                          3.7.1
en-core-web-sm                          3.7.1
entrypoints                             0.4
et-xmlfile                              1.1.0
etuples                                 0.3.9
exchange_calendars                      4.5.4
executing                               2.0.1
faiss-cpu                               1.8.0
Farama-Notifications                    0.0.4
fastai                                  2.7.14
fastai2                                 0.0.30
fastcore                                1.5.43
fastdownload                            0.0.7
fasteners                               0.19
fastjsonschema                          2.19.1
fastparquet                             2024.2.0
fastprogress                            1.0.3
fasttext                                0.9.2
feature-engine                          1.6.2
featuretools                            1.30.0
filelock                                3.14.0
findiff                                 0.10.0
FixedEffectModel                        0.0.5
FlagEmbedding                           1.2.10
FLAML                                   2.1.2
Flask                                   3.0.3
flatbuffers                             24.3.25
fonttools                               4.53.0
formulaic                               1.0.1
fqdn                                    1.5.1
frozendict                              2.4.4
frozenlist                              1.4.1
fs                                      2.4.16
fsspec                                  2023.10.0
fugue                                   0.9.0
functime                                0.9.5
future                                  1.0.0
fuzzy-c-means                           1.7.2
gast                                    0.5.4
gensim                                  4.3.2
gevent                                  24.2.1
gitdb                                   4.0.11
GitPython                               3.1.43
gluonts                                 0.14.4
google-pasta                            0.2.0
gpflow                                  2.9.1
gplearn                                 0.4.2
gpytorch                                1.11
graphene                                3.3
graphql-core                            3.2.3
graphql-relay                           3.2.0
graphviz                                0.20.1
greenlet                                3.0.3
grpcio                                  1.64.1
gunicorn                                21.2.0
gym                                     0.26.2
gym-notices                             0.0.8
gymnasium                               0.28.1
h11                                     0.14.0
h2o                                     3.46.0.1
h5netcdf                                1.3.0
h5py                                    3.11.0
hmmlearn                                0.3.2
holidays                                0.50
holoviews                               1.18.3
homebase                                1.0.1
hopcroftkarp                            1.2.5
html5lib                                1.1
httpcore                                1.0.5
httpstan                                4.12.0
httpx                                   0.27.0
huggingface-hub                         0.23.2
hurst                                   0.0.5
hvplot                                  0.9.2
hydra-core                              1.3.0
hyperopt                                0.2.7
ibm-cloud-sdk-core                      3.20.1
ibm-platform-services                   0.53.7
iisignature                             0.24
ijson                                   3.2.3
imageio                                 2.34.1
imbalanced-learn                        0.12.0
immutabledict                           4.2.0
importlib_metadata                      7.1.0
importlib_resources                     6.4.0
iniconfig                               2.0.0
injector                                0.21.0
interface-meta                          1.3.0
interpret                               0.5.1
interpret-core                          0.5.1
ipykernel                               6.29.4
ipython                                 8.25.0
ipywidgets                              8.1.2
isoduration                             20.11.0
itsdangerous                            2.2.0
jax                                     0.4.25
jax-jumpy                               1.0.0
jaxlib                                  0.4.25
jaxtyping                               0.2.29
jedi                                    0.19.1
Jinja2                                  3.1.4
joblib                                  1.3.2
json5                                   0.9.25
jsonpatch                               1.33
jsonpath-ng                             1.6.1
jsonpointer                             2.1
jsonschema                              4.21.1
jsonschema-specifications               2023.12.1
jupyter                                 1.0.0
jupyter-console                         6.6.3
jupyter-events                          0.10.0
jupyter-lsp                             2.2.5
jupyter-resource-usage                  1.0.2
jupyter_ai                              2.12.0
jupyter_ai_magics                       2.16.0
jupyter_bokeh                           4.0.0
jupyter_client                          8.6.2
jupyter_core                            5.7.2
jupyter_server                          2.14.1
jupyter_server_terminals                0.5.3
jupyterlab                              4.1.5
jupyterlab_pygments                     0.3.0
jupyterlab_server                       2.27.2
jupyterlab_widgets                      3.0.11
kagglehub                               0.2.5
kaleido                                 0.2.1
keras                                   3.3.3
keras-core                              0.1.7
keras-nlp                               0.12.1
keras-rl                                0.4.2
keras-tcn                               3.5.0
keras-tuner                             1.4.7
kiwisolver                              1.4.5
kmapper                                 2.0.1
korean-lunar-calendar                   0.3.1
kt-legacy                               1.0.5
langchain                               0.1.12
langchain-community                     0.0.38
langchain-core                          0.1.52
langchain-text-splitters                0.0.2
langcodes                               3.4.0
langsmith                               0.1.67
language_data                           1.2.0
lark                                    1.1.9
lazy_loader                             0.4
lazypredict-nightly                     0.3.0
libclang                                18.1.1
lifelines                               0.28.0
lightgbm                                4.3.0
lightning                               2.2.5
lightning-utilities                     0.11.2
lime                                    0.2.0.1
line-profiler                           4.1.2
linear-operator                         0.5.1
linkify-it-py                           2.0.3
livelossplot                            0.5.5
llama-index                             0.10.19
llama-index-agent-openai                0.1.7
llama-index-cli                         0.1.12
llama-index-core                        0.10.43
llama-index-embeddings-openai           0.1.10
llama-index-indices-managed-llama-cloud 0.1.6
llama-index-legacy                      0.9.48
llama-index-llms-openai                 0.1.22
llama-index-multi-modal-llms-openai     0.1.6
llama-index-program-openai              0.1.6
llama-index-question-gen-openai         0.1.3
llama-index-readers-file                0.1.23
llama-index-readers-llama-parse         0.1.4
llama-parse                             0.4.4
llamaindex-py-client                    0.1.19
llvmlite                                0.42.0
locket                                  1.0.0
logical-unification                     0.4.6
lxml                                    5.1.0
lz4                                     4.3.3
Mako                                    1.3.5
mamba-ssm                               1.2.0.post1
MAPIE                                   0.8.3
marisa-trie                             1.1.1
Markdown                                3.6
markdown-it-py                          3.0.0
MarkupSafe                              2.1.5
marshmallow                             3.21.2
matplotlib                              3.7.5
matplotlib-inline                       0.1.7
mdit-py-plugins                         0.4.1
mdurl                                   0.1.2
mgarch                                  0.3.0
miniKanren                              1.0.3
minorminer                              0.2.13
mistune                                 3.0.2
ml-dtypes                               0.3.2
mlflow                                  2.11.1
mlforecast                              0.12.0
mljar-supervised                        1.1.6
mlxtend                                 0.23.1
mmh3                                    2.5.1
modin                                   0.26.1
mplfinance                              0.12.10b0
mpmath                                  1.3.0
msgpack                                 1.0.8
multidict                               6.0.5
multipledispatch                        1.0.0
multiprocess                            0.70.16
multitasking                            0.0.11
murmurhash                              1.0.10
mypy-extensions                         1.0.0
namex                                   0.0.8
nbclient                                0.10.0
nbconvert                               7.16.4
nbformat                                5.10.4
ndindex                                 1.8
nest-asyncio                            1.6.0
networkx                                3.3
neural-tangents                         0.6.5
neuralprophet                           0.8.0
nfoursid                                1.0.1
ngboost                                 0.5.1
ninja                                   1.11.1.1
nltk                                    3.8.1
nolds                                   0.5.2
nose                                    1.3.7
notebook                                7.1.3
notebook_shim                           0.2.4
numba                                   0.59.0
numerapi                                2.18.0
numexpr                                 2.10.0
numpy                                   1.26.4
nvidia-cublas-cu12                      12.1.3.1
nvidia-cuda-cupti-cu12                  12.1.105
nvidia-cuda-nvrtc-cu12                  12.1.105
nvidia-cuda-runtime-cu12                12.1.105
nvidia-cudnn-cu12                       8.9.2.26
nvidia-cufft-cu12                       11.0.2.54
nvidia-curand-cu12                      10.3.2.106
nvidia-cusolver-cu12                    11.4.5.107
nvidia-cusparse-cu12                    12.1.0.106
nvidia-nccl-cu12                        2.19.3
nvidia-nvjitlink-cu12                   12.5.40
nvidia-nvtx-cu12                        12.1.105
oauthlib                                3.2.2
omegaconf                               2.3.0
openai                                  1.30.4
opencv-contrib-python-headless          4.9.0.80
opencv-python                           4.10.0.82
openpyxl                                3.1.2
opt-einsum                              3.3.0
optree                                  0.11.0
optuna                                  3.5.0
orjson                                  3.10.3
ortools                                 9.9.3963
osqp                                    0.6.7
overrides                               7.7.0
packaging                               23.2
pandas                                  2.1.4
pandas-flavor                           0.6.0
pandas-ta                               0.3.14b0
pandas_market_calendars                 4.4.0
pandocfilters                           1.5.1
panel                                   1.3.8
param                                   2.1.0
parso                                   0.8.4
partd                                   1.4.2
pastel                                  0.2.1
pathos                                  0.3.2
patsy                                   0.5.6
pbr                                     6.0.0
peewee                                  3.17.3
peft                                    0.11.1
penaltymodel                            1.1.0
PennyLane                               0.35.1
PennyLane-qiskit                        0.35.1
PennyLane_Lightning                     0.35.1
persim                                  0.3.5
pexpect                                 4.9.0
pgmpy                                   0.1.25
pillow                                  10.3.0
pingouin                                0.5.4
plotly                                  5.20.0
plotly-resampler                        0.10.0
plucky                                  0.4.3
pluggy                                  1.5.0
ply                                     3.11
pmdarima                                2.0.4
polars                                  0.20.15
pomegranate                             1.0.4
POT                                     0.9.3
pox                                     0.3.4
ppft                                    1.7.6.8
pprofile                                2.1.0
preshed                                 3.0.9
prometheus_client                       0.20.0
prompt_toolkit                          3.0.45
prophet                                 1.1.5
protobuf                                4.25.3
psutil                                  5.9.8
ptvsd                                   4.3.2
ptyprocess                              0.7.0
PuLP                                    2.8.0
pure-eval                               0.2.2
py-cpuinfo                              9.0.0
py-heat                                 0.0.6
py-heat-magic                           0.0.2
py-lets-be-rational                     1.0.1
py-vollib                               1.0.1
py4j                                    0.10.9.7
pyaml                                   24.4.0
pyarrow                                 15.0.1
pyarrow-hotfix                          0.6
pybind11                                2.12.0
pycaret                                 3.3.2
pydantic                                2.7.3
pydantic_core                           2.18.4
pydevd-pycharm                          231.9225.15
pydmd                                   1.0.0
pyerfa                                  2.0.1.4
pyfolio-reloaded                        0.9.5
Pygments                                2.18.0
PyJWT                                   2.8.0
pykalman                                0.9.7
pylev                                   1.4.0
pyluach                                 2.2.0
pymannkendall                           1.4.3
pymc                                    5.10.4
pymdptoolbox                            4.0b3
pynndescent                             0.5.12
pyod                                    2.0.0
Pyomo                                   6.7.1
pyparsing                               3.1.2
pypdf                                   4.2.0
pyportfolioopt                          1.5.5
pyre-extensions                         0.0.30
pyro-api                                0.1.2
pyro-ppl                                1.9.0
pysimdjson                              5.0.2
pyspnego                                0.10.2
pystan                                  3.9.0
pytensor                                2.18.6
pytest                                  8.2.1
python-dateutil                         2.9.0.post0
python-dotenv                           1.0.0
python-json-logger                      2.0.7
python-statemachine                     2.1.2
pytorch-ignite                          0.4.13
pytorch-lightning                       1.9.5
pytorch-tabnet                          4.1.0
pytz                                    2024.1
pyvinecopulib                           0.6.5
pyviz_comms                             3.0.2
PyWavelets                              1.5.0
PyYAML                                  6.0.1
pyzmq                                   26.0.3
qdldl                                   0.1.7.post2
qiskit                                  1.0.2
qiskit-aer                              0.14.2
qiskit-ibm-provider                     0.11.0
qiskit-ibm-runtime                      0.20.0
qtconsole                               5.5.2
QtPy                                    2.4.1
quadprog                                0.1.12
quantecon                               0.7.2
QuantLib                                1.33
QuantStats                              0.0.62
querystring-parser                      1.2.4
rauth                                   0.7.3
ray                                     2.9.3
Rbeast                                  0.1.19
rectangle-packer                        2.0.2
referencing                             0.35.1
regex                                   2024.5.15
requests-ntlm                           1.2.0
requests-oauthlib                       1.3.1
retrying                                1.3.4
rfc3339-validator                       0.1.4
rfc3986-validator                       0.1.1
rich                                    13.7.1
ripser                                  0.6.8
Riskfolio-Lib                           6.0.0
riskparityportfolio                     0.5.1
river                                   0.21.0
rpds-py                                 0.18.1
ruptures                                1.1.9
rustworkx                               0.14.2
safetensors                             0.4.3
SALib                                   1.5.0
schemdraw                               0.15
scikeras                                0.13.0
scikit-base                             0.7.8
scikit-image                            0.22.0
scikit-learn                            1.4.2
scikit-learn-extra                      0.3.0
scikit-optimize                         0.10.0
scikit-plot                             0.3.7
scikit-tda                              1.0.0
scipy                                   1.11.4
scs                                     3.2.4.post2
sdeint                                  0.3.0
seaborn                                 0.13.2
semantic-version                        2.10.0
Send2Trash                              1.8.3
sentence-transformers                   3.0.0
setuptools-scm                          8.1.0
shap                                    0.45.0
Shimmy                                  1.3.0
simplejson                              3.19.2
simpy                                   4.1.1
six                                     1.16.0
sklearn-json                            0.1.0
sktime                                  0.26.0
slicer                                  0.0.7
smart-open                              6.4.0
smmap                                   5.0.1
sniffio                                 1.3.1
sortedcontainers                        2.4.0
soupsieve                               2.5
spacy                                   3.7.4
spacy-legacy                            3.0.12
spacy-loggers                           1.0.5
SQLAlchemy                              2.0.30
sqlparse                                0.5.0
srsly                                   2.4.8
stable_baselines3                       2.3.2
stack-data                              0.6.3
stanio                                  0.3.0
statsforecast                           1.7.5
statsmodels                             0.14.1
stevedore                               5.2.0
stochastic                              0.6.0
stockstats                              0.6.2
stopit                                  1.1.2
striprtf                                0.0.26
stumpy                                  1.12.0
symengine                               0.11.0
sympy                                   1.12.1
ta                                      0.11.0
TA-Lib                                  0.4.28
tables                                  3.9.2
tabulate                                0.8.10
tadasets                                0.2.1
tbats                                   1.1.3
tblib                                   3.0.0
tenacity                                8.3.0
tensorboard                             2.16.2
tensorboard-data-server                 0.7.2
tensorboardX                            2.6.2.2
tensorflow                              2.16.1
tensorflow-addons                       0.23.0
tensorflow-io-gcs-filesystem            0.37.0
tensorflow-probability                  0.24.0
tensorflow-text                         2.16.1
tensorflow_decision_forests             1.9.0
tensorly                                0.8.1
tensorrt                                8.6.1.post1
tensorrt-bindings                       8.6.1
tensorrt-libs                           8.6.1
tensortrade                             1.0.3
termcolor                               2.4.0
terminado                               0.18.1
tf2jax                                  0.3.6
tf_keras                                2.16.0
thinc                                   8.2.3
threadpoolctl                           3.5.0
thundergbm                              0.3.17
tifffile                                2024.5.22
tiktoken                                0.7.0
tinycss2                                1.3.0
tokenizers                              0.19.1
toml                                    0.10.2
toolz                                   0.12.1
torch                                   2.2.1
torch-cluster                           1.6.3
torch-scatter                           2.1.2
torch-sparse                            0.6.18
torch-spline-conv                       1.2.2
torch_geometric                         2.5.1
torchdata                               0.7.1
torchmetrics                            1.4.0.post0
torchvision                             0.17.1
tornado                                 6.4
TPOT                                    0.12.2
tqdm                                    4.66.4
traitlets                               5.14.3
transformers                            4.40.2
triad                                   0.9.6
triton                                  2.2.0
tsdownsample                            0.1.3
tsfresh                                 0.20.2
tslearn                                 0.6.3
tweepy                                  4.14.0
typeguard                               2.13.3
typer                                   0.9.4
typer-config                            1.4.0
types-python-dateutil                   2.9.0.20240316
typing-inspect                          0.9.0
typing_extensions                       4.12.1
tzdata                                  2024.1
uc-micro-py                             1.0.3
umap-learn                              0.5.5
update-checker                          0.18.0
uri-template                            1.3.0
utilsforecast                           0.1.10
wasabi                                  1.1.3
wcwidth                                 0.2.13
weasel                                  0.3.4
webargs                                 8.4.0
webcolors                               1.13
webencodings                            0.5.1
websocket-client                        1.8.0
websockets                              12.0
Werkzeug                                3.0.1
widgetsnbextension                      4.0.11
window_ops                              0.0.15
woodwork                                0.31.0
wordcloud                               1.9.3
wrapt                                   1.16.0
wurlitzer                               3.1.0
x-transformers                          1.30.4
xarray                                  2024.2.0
xarray-einstats                         0.7.0
xgboost                                 2.0.3
xlrd                                    2.0.1
XlsxWriter                              3.2.0
xxhash                                  3.4.1
xyzservices                             2024.4.0
yarl                                    1.9.4
yellowbrick                             1.5
yfinance                                0.2.40
zict                                    3.0.0
zipp                                    3.19.1
zope.event                              5.0
zope.interface                          6.4.post2

Accord                               3.6.0
Accord.Fuzzy                         3.6.0
Accord.MachineLearning               3.6.0
Accord.Math                          3.6.0
Accord.Statistics                    3.6.0
CloneExtensions                      1.3.0
Common.Logging                       3.4.1
Common.Logging.Core                  3.4.1
CsvHelper                            19.0.0
Deedle                               2.1.0
DotNetZip                            1.16.0
DynamicInterop                       0.9.1
fasterflect                          3.0.0
MathNet.Numerics                     5.0.0
McMaster.Extensions.CommandLineUtils 2.6.0
Microsoft.IO.RecyclableMemoryStream  2.3.2
Microsoft.NET.Test.Sdk               16.9.4
Microsoft.TestPlatform.ObjectModel   16.9.4
Moq                                  4.16.1
NetMQ                                4.0.1.6
Newtonsoft.Json                      13.0.2
NodaTime                             3.0.5
NUnit                                3.13.3
NUnit3TestAdapter                    4.2.1
Plotly.NET                           3.0.1
Plotly.NET.Interactive               3.0.2
protobuf-net                         3.1.3
QLNet                                1.13.0
QuantConnect.pythonnet               2.0.38
RestSharp                            106.12.0
SharpZipLib                          1.3.3
System.ComponentModel.Composition    6.0.0

Autokeras Environment

The Autokeras environment provides a backwards compatability environment for compatibility with


    Keras < 3


    Tensorflow 2.14.1

, and


    Pydantic

. This environment supports the following libraries:

absl-py                                 2.1.0
accelerate                              0.30.1
adagio                                  0.2.4
aesara                                  2.9.3
aiohttp                                 3.9.5
aiosignal                               1.3.1
aiosqlite                               0.20.0
alibi-detect                            0.12.0
alphalens-reloaded                      0.4.3
altair                                  5.2.0
aniso8601                               9.0.1
annotated-types                         0.7.0
antlr4-python3-runtime                  4.9.3
anyio                                   4.4.0
appdirs                                 1.4.4
apricot-select                          0.6.1
arch                                    6.3.0
argon2-cffi                             23.1.0
argon2-cffi-bindings                    21.2.0
arrow                                   1.3.0
arviz                                   0.18.0
astropy                                 6.0.0
astropy-iers-data                       0.2024.6.3.0.31.14
asttokens                               2.4.1
astunparse                              1.6.3
async-generator                         1.10
async-lru                               2.0.4
Authlib                                 1.3.0
autograd                                1.6.2
autograd-gamma                          0.5.0
autokeras                               1.1.0
autoray                                 0.6.12
ax-platform                             0.3.7
Babel                                   2.15.0
bayesian-optimization                   1.4.3
beautifulsoup4                          4.12.3
bleach                                  6.1.0
blis                                    0.7.11
blosc2                                  2.6.2
bokeh                                   3.3.4
botorch                                 0.10.0
Bottleneck                              1.3.8
cachetools                              5.3.3
captum                                  0.7.0
catalogue                               2.0.10
catboost                                1.2.3
category-encoders                       2.6.3
causal-conv1d                           1.2.0.post2
certipy                                 0.1.3
chardet                                 5.2.0
check-shapes                            1.1.1
clarabel                                0.9.0
click                                   8.1.7
clikit                                  0.6.2
cloudpathlib                            0.16.0
cloudpickle                             3.0.0
clr-loader                              0.1.6
cmdstanpy                               1.2.1
colorama                                0.4.6
colorcet                                3.1.0
colorlog                                6.8.2
colorlover                              0.3.0
colour                                  0.1.5
comm                                    0.2.2
confection                              0.1.5
cons                                    0.4.6
contourpy                               1.2.0
control                                 0.9.4
copulae                                 0.7.9
copulas                                 0.10.1
coreforecast                            0.0.9
cramjam                                 2.8.3
crashtest                               0.3.1
creme                                   0.6.1
cufflinks                               0.17.3
cvxopt                                  1.3.2
cvxpy                                   1.4.2
cycler                                  0.12.1
cymem                                   2.0.8
Cython                                  3.0.9
darts                                   0.28.0
dash                                    2.17.0
dash-core-components                    2.0.0
dash-cytoscape                          1.0.1
dash-html-components                    2.0.0
dash-table                              5.0.0
dask                                    2024.3.1
dask-expr                               1.0.5
dataclasses-json                        0.6.6
datasets                                2.17.1
deap                                    1.4.1
debugpy                                 1.8.1
decorator                               5.1.1
deepmerge                               1.1.1
defusedxml                              0.7.1
Deprecated                              1.2.14
deprecation                             2.1.0
dgl                                     2.1.0
dill                                    0.3.8
dimod                                   0.12.14
dirtyjson                               1.0.8
diskcache                               5.6.3
distributed                             2024.3.1
dm-tree                                 0.1.8
docker                                  7.1.0
docutils                                0.20.1
DoubleML                                0.7.1
dropstackframe                          0.1.0
dtreeviz                                2.2.2
dtw-python                              1.3.1
dwave-cloud-client                      0.11.3
dwave-drivers                           0.4.4
dwave-greedy                            0.3.0
dwave-hybrid                            0.6.11
dwave-inspector                         0.4.4
dwave-inspectorapp                      0.3.1
dwave-neal                              0.6.0
dwave-networkx                          0.8.14
dwave-ocean-sdk                         6.9.0
dwave-preprocessing                     0.6.5
dwave-samplers                          1.2.0
dwave-system                            1.23.0
dwave-tabu                              0.5.0
dwavebinarycsp                          0.3.0
ecos                                    2.0.13
einops                                  0.7.0
EMD-signal                              1.6.0
empyrical-reloaded                      0.5.10
en-core-web-md                          3.7.1
en-core-web-sm                          3.7.1
et-xmlfile                              1.1.0
etuples                                 0.3.9
exchange_calendars                      4.5.4
executing                               2.0.1
faiss-cpu                               1.8.0
Farama-Notifications                    0.0.4
fastai                                  2.7.14
fastai2                                 0.0.30
fastcore                                1.5.43
fastdownload                            0.0.7
fasteners                               0.19
fastjsonschema                          2.19.1
fastparquet                             2024.2.0
fastprogress                            1.0.3
fasttext                                0.9.2
feature-engine                          1.6.2
featuretools                            1.30.0
filelock                                3.14.0
findiff                                 0.10.0
FixedEffectModel                        0.0.5
FlagEmbedding                           1.2.10
FLAML                                   2.1.2
Flask                                   3.0.3
flatbuffers                             24.3.25
fonttools                               4.53.0
formulaic                               1.0.1
fqdn                                    1.5.1
frozendict                              2.4.4
frozenlist                              1.4.1
fs                                      2.4.16
fsspec                                  2023.10.0
fugue                                   0.9.0
functime                                0.9.5
future                                  1.0.0
fuzzy-c-means                           1.7.2
gast                                    0.5.4
gensim                                  4.3.2
gevent                                  24.2.1
gitdb                                   4.0.11
GitPython                               3.1.43
gluonts                                 0.14.4
google-auth                             2.29.0
google-auth-oauthlib                    1.0.0
google-pasta                            0.2.0
gpflow                                  2.9.1
gplearn                                 0.4.2
gpytorch                                1.11
graphene                                3.3
graphql-core                            3.2.3
graphql-relay                           3.2.0
graphviz                                0.20.1
grpcio                                  1.64.1
gunicorn                                21.2.0
gym                                     0.26.2
gym-notices                             0.0.8
gymnasium                               0.28.1
h11                                     0.14.0
h2o                                     3.46.0.1
h5netcdf                                1.3.0
h5py                                    3.11.0
hmmlearn                                0.3.2
holidays                                0.50
holoviews                               1.18.3
homebase                                1.0.1
hopcroftkarp                            1.2.5
html5lib                                1.1
httpcore                                1.0.5
httpstan                                4.12.0
httpx                                   0.27.0
huggingface-hub                         0.23.2
hurst                                   0.0.5
hvplot                                  0.9.2
hydra-core                              1.3.0
hyperopt                                0.2.7
ibm-cloud-sdk-core                      3.20.1
ibm-platform-services                   0.53.7
iisignature                             0.24
ijson                                   3.2.3
imageio                                 2.34.1
imbalanced-learn                        0.12.0
immutabledict                           4.2.0
importlib-metadata                      4.13.0
iniconfig                               2.0.0
injector                                0.21.0
interface-meta                          1.3.0
interpret                               0.5.1
interpret-core                          0.5.1
ipykernel                               6.29.4
ipython                                 8.25.0
ipywidgets                              8.1.2
isoduration                             20.11.0
itsdangerous                            2.2.0
jax                                     0.4.25
jax-jumpy                               1.0.0
jaxlib                                  0.4.25
jaxtyping                               0.2.29
jedi                                    0.19.1
joblib                                  1.3.2
json5                                   0.9.25
jsonpatch                               1.33
jsonpath-ng                             1.6.1
jsonpointer                             2.1
jupyter                                 1.0.0
jupyter-console                         6.6.3
jupyter-events                          0.10.0
jupyter-lsp                             2.2.5
jupyter-resource-usage                  1.0.2
jupyter_ai                              2.12.0
jupyter_ai_magics                       2.16.0
jupyter_bokeh                           4.0.0
jupyter_client                          8.6.2
jupyter_core                            5.7.2
jupyter_server                          2.14.1
jupyter_server_terminals                0.5.3
jupyterlab                              4.1.5
jupyterlab_pygments                     0.3.0
jupyterlab_server                       2.27.2
jupyterlab_widgets                      3.0.11
kagglehub                               0.2.5
kaleido                                 0.2.1
keras                                   2.14.0
keras-core                              0.1.7
keras-nlp                               0.12.1
keras-rl                                0.4.2
keras-tcn                               3.5.0
keras-tuner                             1.4.7
kiwisolver                              1.4.5
kmapper                                 2.0.1
korean-lunar-calendar                   0.3.1
kt-legacy                               1.0.5
langchain                               0.1.12
langchain-community                     0.0.38
langchain-core                          0.1.52
langchain-text-splitters                0.0.2
langcodes                               3.4.0
langsmith                               0.1.67
language_data                           1.2.0
lark                                    1.1.9
lazy_loader                             0.4
lazypredict-nightly                     0.3.0
libclang                                18.1.1
lifelines                               0.28.0
lightgbm                                4.3.0
lightning                               2.2.5
lightning-utilities                     0.11.2
lime                                    0.2.0.1
line-profiler                           4.1.2
linear-operator                         0.5.1
linkify-it-py                           2.0.3
livelossplot                            0.5.5
llama-index                             0.10.19
llama-index-agent-openai                0.1.7
llama-index-cli                         0.1.12
llama-index-core                        0.10.43
llama-index-embeddings-openai           0.1.10
llama-index-indices-managed-llama-cloud 0.1.6
llama-index-legacy                      0.9.48
llama-index-llms-openai                 0.1.22
llama-index-multi-modal-llms-openai     0.1.6
llama-index-program-openai              0.1.6
llama-index-question-gen-openai         0.1.3
llama-index-readers-file                0.1.23
llama-index-readers-llama-parse         0.1.4
llama-parse                             0.4.4
llamaindex-py-client                    0.1.19
llvmlite                                0.42.0
locket                                  1.0.0
logical-unification                     0.4.6
lxml                                    5.1.0
lz4                                     4.3.3
mamba-ssm                               1.2.0.post1
MAPIE                                   0.8.3
marisa-trie                             1.1.1
Markdown                                3.6
markdown-it-py                          3.0.0
marshmallow                             3.21.2
matplotlib                              3.7.5
matplotlib-inline                       0.1.7
mdit-py-plugins                         0.4.1
mdurl                                   0.1.2
mgarch                                  0.3.0
miniKanren                              1.0.3
minorminer                              0.2.13
mistune                                 3.0.2
ml-dtypes                               0.2.0
mlflow                                  2.11.1
mlforecast                              0.12.0
mljar-supervised                        1.1.6
mlxtend                                 0.23.1
mmh3                                    2.5.1
modin                                   0.26.1
mplfinance                              0.12.10b0
mpmath                                  1.3.0
msgpack                                 1.0.8
multidict                               6.0.5
multipledispatch                        1.0.0
multiprocess                            0.70.16
multitasking                            0.0.11
murmurhash                              1.0.10
mypy-extensions                         1.0.0
namex                                   0.0.8
nbclient                                0.10.0
nbconvert                               7.16.4
nbformat                                5.10.4
ndindex                                 1.8
nest-asyncio                            1.6.0
networkx                                3.3
neural-tangents                         0.6.5
neuralprophet                           0.8.0
nfoursid                                1.0.1
ngboost                                 0.5.1
ninja                                   1.11.1.1
nixtlats                                0.2.0
nltk                                    3.8.1
nolds                                   0.5.2
nose                                    1.3.7
notebook                                7.1.3
notebook_shim                           0.2.4
numba                                   0.59.0
numerapi                                2.18.0
numexpr                                 2.10.0
numpy                                   1.26.4
nvidia-cublas-cu12                      12.5.2.13
nvidia-cuda-cupti-cu12                  12.1.105
nvidia-cuda-nvrtc-cu12                  12.1.105
nvidia-cuda-runtime-cu12                12.1.105
nvidia-cudnn-cu12                       8.9.2.26
nvidia-cufft-cu12                       11.0.2.54
nvidia-curand-cu12                      10.3.2.106
nvidia-cusolver-cu12                    11.4.5.107
nvidia-cusparse-cu12                    12.1.0.106
nvidia-nccl-cu12                        2.19.3
nvidia-nvjitlink-cu12                   12.5.40
nvidia-nvtx-cu12                        12.1.105
omegaconf                               2.3.0
openai                                  1.30.4
opencv-contrib-python-headless          4.9.0.80
opencv-python                           4.10.0.82
openpyxl                                3.1.2
opt-einsum                              3.3.0
optree                                  0.11.0
optuna                                  3.5.0
orjson                                  3.10.3
ortools                                 9.9.3963
osqp                                    0.6.7
overrides                               7.7.0
packaging                               23.2
pandas                                  2.1.4
pandas-flavor                           0.6.0
pandas-ta                               0.3.14b0
pandas_market_calendars                 4.4.0
pandocfilters                           1.5.1
panel                                   1.3.8
param                                   2.1.0
parso                                   0.8.4
partd                                   1.4.2
pastel                                  0.2.1
pathos                                  0.3.2
patsy                                   0.5.6
pbr                                     6.0.0
pearl                                   2.3.12
peewee                                  3.17.3
peft                                    0.11.1
penaltymodel                            1.1.0
PennyLane                               0.35.1
PennyLane-qiskit                        0.35.1
PennyLane_Lightning                     0.35.1
persim                                  0.3.5
pexpect                                 4.9.0
pgmpy                                   0.1.25
pillow                                  10.3.0
pingouin                                0.5.4
plotly                                  5.20.0
plotly-resampler                        0.10.0
plucky                                  0.4.3
pluggy                                  1.5.0
ply                                     3.11
pmdarima                                2.0.4
polars                                  0.20.15
pomegranate                             1.0.4
POT                                     0.9.3
pox                                     0.3.4
ppft                                    1.7.6.8
pprofile                                2.1.0
preshed                                 3.0.9
prompt_toolkit                          3.0.45
prophet                                 1.1.5
protobuf                                4.25.3
ptyprocess                              0.7.0
PuLP                                    2.8.0
pure-eval                               0.2.2
py-cpuinfo                              9.0.0
py-heat                                 0.0.6
py-heat-magic                           0.0.2
py-lets-be-rational                     1.0.1
py-vollib                               1.0.1
py4j                                    0.10.9.7
pyaml                                   24.4.0
pyarrow                                 15.0.1
pyarrow-hotfix                          0.6
pyasn1                                  0.6.0
pyasn1_modules                          0.4.0
pybind11                                2.12.0
pycaret                                 3.3.2
pycparser                               2.20
pydantic                                1.10.15
pydantic_core                           2.18.4
pydmd                                   1.0.0
pyerfa                                  2.0.1.4
pyfolio-reloaded                        0.9.5
Pygments                                2.18.0
pykalman                                0.9.7
pylev                                   1.4.0
pyluach                                 2.2.0
pymannkendall                           1.4.3
pymc                                    5.10.4
pymdptoolbox                            4.0b3
pynndescent                             0.5.12
pyod                                    2.0.0
Pyomo                                   6.7.1
pyparsing                               3.1.2
pypdf                                   4.2.0
pyportfolioopt                          1.5.5
pyre-extensions                         0.0.30
pyro-api                                0.1.2
pyro-ppl                                1.9.0
pysimdjson                              5.0.2
pyspnego                                0.10.2
pystan                                  3.9.0
pytensor                                2.18.6
pytest                                  8.2.1
python-dotenv                           1.0.0
python-statemachine                     2.1.2
pytorch-ignite                          0.4.13
pytorch-lightning                       1.9.5
pytorch-tabnet                          4.1.0
pytz                                    2024.1
pyvinecopulib                           0.6.5
pyviz_comms                             3.0.2
PyWavelets                              1.5.0
PyYAML                                  6.0.1
pyzmq                                   26.0.3
qdldl                                   0.1.7.post2
qiskit                                  1.0.2
qiskit-aer                              0.14.2
qiskit-ibm-provider                     0.11.0
qiskit-ibm-runtime                      0.20.0
qtconsole                               5.5.2
QtPy                                    2.4.1
quadprog                                0.1.12
quantconnect-stubs                      16467
quantecon                               0.7.2
QuantLib                                1.33
QuantStats                              0.0.62
querystring-parser                      1.2.4
rauth                                   0.7.3
ray                                     2.9.3
Rbeast                                  0.1.19
rectangle-packer                        2.0.2
regex                                   2024.5.15
requests-ntlm                           1.2.0
requests-oauthlib                       1.3.1
retrying                                1.3.4
rfc3339-validator                       0.1.4
rfc3986-validator                       0.1.1
rich                                    13.7.1
ripser                                  0.6.8
Riskfolio-Lib                           6.0.0
riskparityportfolio                     0.5.1
river                                   0.21.0
rsa                                     4.9
ruptures                                1.1.9
rustworkx                               0.14.2
safetensors                             0.4.3
SALib                                   1.5.0
schemdraw                               0.15
scikeras                                0.13.0
scikit-base                             0.7.8
scikit-image                            0.22.0
scikit-learn                            1.4.2
scikit-learn-extra                      0.3.0
scikit-optimize                         0.10.0
scikit-plot                             0.3.7
scikit-tda                              1.0.0
scipy                                   1.11.4
scs                                     3.2.4.post2
sdeint                                  0.3.0
seaborn                                 0.13.2
semantic-version                        2.10.0
Send2Trash                              1.8.3
sentence-transformers                   3.0.0
setuptools-scm                          8.1.0
shap                                    0.45.0
Shimmy                                  1.3.0
simplejson                              3.19.2
simpy                                   4.1.1
sklearn-json                            0.1.0
sktime                                  0.26.0
slicer                                  0.0.7
smart-open                              6.4.0
smmap                                   5.0.1
sniffio                                 1.3.1
sortedcontainers                        2.4.0
soupsieve                               2.5
spacy                                   3.7.4
spacy-legacy                            3.0.12
spacy-loggers                           1.0.5
sqlparse                                0.5.0
srsly                                   2.4.8
stable_baselines3                       2.3.2
stack-data                              0.6.3
stanio                                  0.3.0
statsforecast                           1.7.5
statsmodels                             0.14.1
stevedore                               5.2.0
stochastic                              0.6.0
stockstats                              0.6.2
stopit                                  1.1.2
striprtf                                0.0.26
stumpy                                  1.12.0
symengine                               0.11.0
sympy                                   1.12.1
ta                                      0.11.0
TA-Lib                                  0.4.28
tables                                  3.9.2
tabulate                                0.8.10
tadasets                                0.2.1
tbats                                   1.1.3
tblib                                   3.0.0
tenacity                                8.3.0
tensorboard                             2.14.1
tensorboard-data-server                 0.7.2
tensorboardX                            2.6.2.2
tensorflow                              2.14.1
tensorflow-addons                       0.23.0
tensorflow-estimator                    2.14.0
tensorflow-hub                          0.16.1
tensorflow-io-gcs-filesystem            0.37.0
tensorflow-probability                  0.24.0
tensorflow-ranking                      0.5.3
tensorflow-serving-api                  2.14.1
tensorflow-text                         2.14.0
tensorflow_decision_forests             1.9.0
tensorly                                0.8.1
tensorrt                                8.6.1.post1
tensorrt-bindings                       8.6.1
tensorrt-libs                           8.6.1
tensortrade                             1.0.3
termcolor                               2.4.0
terminado                               0.18.1
tf-keras                                2.15.0
tf2jax                                  0.3.6
thinc                                   8.2.3
threadpoolctl                           3.5.0
thundergbm                              0.3.17
tifffile                                2024.5.22
tiktoken                                0.7.0
tinycss2                                1.3.0
tokenizers                              0.19.1
toml                                    0.10.2
toolz                                   0.12.1
torch                                   2.2.1
torch-cluster                           1.6.3
torch-scatter                           2.1.2
torch-sparse                            0.6.18
torch-spline-conv                       1.2.2
torch_geometric                         2.5.1
torchdata                               0.7.1
torchmetrics                            1.4.0.post0
torchvision                             0.17.1
TPOT                                    0.12.2
tqdm                                    4.66.4
transformers                            4.40.2
triad                                   0.9.6
triton                                  2.2.0
tsdownsample                            0.1.3
tsfresh                                 0.20.2
tslearn                                 0.6.3
tweepy                                  4.14.0
typeguard                               2.13.3
typer                                   0.9.4
typer-config                            1.4.0
types-python-dateutil                   2.9.0.20240316
typing-inspect                          0.9.0
tzdata                                  2024.1
uc-micro-py                             1.0.3
umap-learn                              0.5.5
update-checker                          0.18.0
uri-template                            1.3.0
utilsforecast                           0.1.10
wasabi                                  1.1.3
wcwidth                                 0.2.13
weasel                                  0.3.4
webargs                                 8.4.0
webcolors                               1.13
webencodings                            0.5.1
websocket-client                        1.8.0
websockets                              12.0
Werkzeug                                3.0.1
widgetsnbextension                      4.0.11
window_ops                              0.0.15
woodwork                                0.31.0
wordcloud                               1.9.3
wrapt                                   1.14.1
wurlitzer                               3.1.0
x-transformers                          1.30.4
xarray                                  2024.2.0
xarray-einstats                         0.7.0
xgboost                                 2.0.3
xlrd                                    2.0.1
XlsxWriter                              3.2.0
xxhash                                  3.4.1
xyzservices                             2024.4.0
yarl                                    1.9.4
yellowbrick                             1.5
yfinance                                0.2.40
zict                                    3.0.0
zope.event                              5.0
zope.interface                          6.4.post2

Autogluon Environment

absl-py                                 2.1.0
accelerate                              0.21.0
adagio                                  0.2.4
aesara                                  2.9.3
aiohttp                                 3.9.5
aiohttp-cors                            0.7.0
aiosignal                               1.3.1
aiosqlite                               0.20.0
alibi-detect                            0.12.0
aliyun-python-sdk-core                  2.15.1
aliyun-python-sdk-kms                   2.16.3
alphalens-reloaded                      0.4.3
altair                                  5.2.0
aniso8601                               9.0.1
annotated-types                         0.7.0
antlr4-python3-runtime                  4.9.3
anyio                                   4.4.0
appdirs                                 1.4.4
apricot-select                          0.6.1
arch                                    6.3.0
argon2-cffi                             23.1.0
argon2-cffi-bindings                    21.2.0
arrow                                   1.3.0
arviz                                   0.18.0
astropy                                 6.0.0
astropy-iers-data                       0.2024.6.3.0.31.14
asttokens                               2.4.1
astunparse                              1.6.3
async-generator                         1.10
async-lru                               2.0.4
Authlib                                 1.3.0
autogluon                               1.1.0
autogluon.common                        1.1.0
autogluon.core                          1.1.0
autogluon.features                      1.1.0
autogluon.multimodal                    1.1.0
autogluon.tabular                       1.1.0
autogluon.timeseries                    1.1.0
autograd                                1.6.2
autograd-gamma                          0.5.0
autokeras                               2.0.0
autoray                                 0.6.12
ax-platform                             0.3.7
Babel                                   2.15.0
bayesian-optimization                   1.4.3
beautifulsoup4                          4.12.3
bleach                                  6.1.0
blis                                    0.7.11
blosc2                                  2.6.2
bokeh                                   3.3.4
boto3                                   1.34.118
botocore                                1.34.118
botorch                                 0.10.0
Bottleneck                              1.3.8
cachetools                              5.3.3
captum                                  0.7.0
catalogue                               2.0.10
catboost                                1.2.3
category-encoders                       2.6.3
causal-conv1d                           1.2.0.post2
certipy                                 0.1.3
chardet                                 5.2.0
check-shapes                            1.1.1
clarabel                                0.9.0
click                                   8.1.7
clikit                                  0.6.2
cloudpathlib                            0.16.0
cloudpickle                             3.0.0
clr-loader                              0.1.6
cmdstanpy                               1.2.1
colorama                                0.4.6
colorcet                                3.1.0
coloredlogs                             15.0.1
colorful                                0.5.6
colorlog                                6.8.2
colorlover                              0.3.0
colour                                  0.1.5
comm                                    0.2.2
confection                              0.1.5
cons                                    0.4.6
contourpy                               1.2.0
control                                 0.9.4
copulae                                 0.7.9
copulas                                 0.10.1
coreforecast                            0.0.9
cramjam                                 2.8.3
crashtest                               0.3.1
crcmod                                  1.7
creme                                   0.6.1
cufflinks                               0.17.3
cvxopt                                  1.3.2
cvxpy                                   1.4.2
cycler                                  0.12.1
cymem                                   2.0.8
Cython                                  3.0.9
darts                                   0.28.0
dash                                    2.17.0
dash-core-components                    2.0.0
dash-cytoscape                          1.0.1
dash-html-components                    2.0.0
dash-table                              5.0.0
dask                                    2024.3.1
dask-expr                               1.0.5
dataclasses-json                        0.6.6
datasets                                2.17.1
deap                                    1.4.1
debugpy                                 1.8.1
decorator                               5.1.1
deepmerge                               1.1.1
defusedxml                              0.7.1
Deprecated                              1.2.14
deprecation                             2.1.0
dgl                                     2.1.0
dill                                    0.3.8
dimod                                   0.12.14
dirtyjson                               1.0.8
diskcache                               5.6.3
distlib                                 0.3.8
distributed                             2024.3.1
dm-tree                                 0.1.8
docker                                  7.1.0
docutils                                0.20.1
DoubleML                                0.7.1
dropstackframe                          0.1.0
dtreeviz                                2.2.2
dtw-python                              1.3.1
dwave-cloud-client                      0.11.3
dwave-drivers                           0.4.4
dwave-greedy                            0.3.0
dwave-hybrid                            0.6.11
dwave-inspector                         0.4.4
dwave-inspectorapp                      0.3.1
dwave-neal                              0.6.0
dwave-networkx                          0.8.14
dwave-ocean-sdk                         6.9.0
dwave-preprocessing                     0.6.5
dwave-samplers                          1.2.0
dwave-system                            1.23.0
dwave-tabu                              0.5.0
dwavebinarycsp                          0.3.0
ecos                                    2.0.13
einops                                  0.7.0
EMD-signal                              1.6.0
empyrical-reloaded                      0.5.10
en-core-web-md                          3.7.1
en-core-web-sm                          3.7.1
et-xmlfile                              1.1.0
etuples                                 0.3.9
evaluate                                0.4.2
exchange_calendars                      4.5.4
executing                               2.0.1
faiss-cpu                               1.8.0
Farama-Notifications                    0.0.4
fastai                                  2.7.14
fastai2                                 0.0.30
fastcore                                1.5.43
fastdownload                            0.0.7
fasteners                               0.19
fastjsonschema                          2.19.1
fastparquet                             2024.2.0
fastprogress                            1.0.3
fasttext                                0.9.2
feature-engine                          1.6.2
featuretools                            1.30.0
filelock                                3.14.0
findiff                                 0.10.0
FixedEffectModel                        0.0.5
FlagEmbedding                           1.2.10
FLAML                                   2.1.2
Flask                                   3.0.3
flatbuffers                             24.3.25
fonttools                               4.53.0
formulaic                               1.0.1
fqdn                                    1.5.1
frozendict                              2.4.4
frozenlist                              1.4.1
fs                                      2.4.16
fsspec                                  2023.10.0
fugue                                   0.9.0
functime                                0.9.5
future                                  1.0.0
fuzzy-c-means                           1.7.2
gast                                    0.5.4
gdown                                   5.2.0
gensim                                  4.3.2
gevent                                  24.2.1
gitdb                                   4.0.11
GitPython                               3.1.43
gluonts                                 0.14.3
google-api-core                         2.19.0
google-auth                             2.29.0
google-pasta                            0.2.0
googleapis-common-protos                1.63.1
gpflow                                  2.9.1
gplearn                                 0.4.2
gpytorch                                1.11
graphene                                3.3
graphql-core                            3.2.3
graphql-relay                           3.2.0
graphviz                                0.20.1
grpcio                                  1.64.1
gunicorn                                21.2.0
gym                                     0.26.2
gym-notices                             0.0.8
gymnasium                               0.28.1
h11                                     0.14.0
h2o                                     3.46.0.1
h5netcdf                                1.3.0
h5py                                    3.11.0
hmmlearn                                0.3.2
holidays                                0.50
holoviews                               1.18.3
homebase                                1.0.1
hopcroftkarp                            1.2.5
html5lib                                1.1
httpcore                                1.0.5
httpstan                                4.12.0
httpx                                   0.27.0
huggingface-hub                         0.23.2
humanfriendly                           10.0
hurst                                   0.0.5
hvplot                                  0.9.2
hydra-core                              1.3.0
hyperopt                                0.2.7
ibm-cloud-sdk-core                      3.20.1
ibm-platform-services                   0.53.7
iisignature                             0.24
ijson                                   3.2.3
imageio                                 2.34.1
imbalanced-learn                        0.12.0
immutabledict                           4.2.0
iniconfig                               2.0.0
injector                                0.21.0
interface-meta                          1.3.0
interpret                               0.5.1
interpret-core                          0.5.1
ipykernel                               6.29.4
ipython                                 8.25.0
ipywidgets                              8.1.2
isoduration                             20.11.0
itsdangerous                            2.2.0
jax                                     0.4.25
jax-jumpy                               1.0.0
jaxlib                                  0.4.25
jaxtyping                               0.2.29
jedi                                    0.19.1
jmespath                                0.10.0
joblib                                  1.3.2
json5                                   0.9.25
jsonpatch                               1.33
jsonpath-ng                             1.6.1
jsonpointer                             2.1
jupyter                                 1.0.0
jupyter-console                         6.6.3
jupyter-events                          0.10.0
jupyter-lsp                             2.2.5
jupyter-resource-usage                  1.0.2
jupyter_ai                              2.12.0
jupyter_ai_magics                       2.16.0
jupyter_bokeh                           4.0.0
jupyter_client                          8.6.2
jupyter_core                            5.7.2
jupyter_server                          2.14.1
jupyter_server_terminals                0.5.3
jupyterlab                              4.1.5
jupyterlab_pygments                     0.3.0
jupyterlab_server                       2.27.2
jupyterlab_widgets                      3.0.11
kagglehub                               0.2.5
kaleido                                 0.2.1
keras                                   3.3.3
keras-core                              0.1.7
keras-nlp                               0.12.1
keras-rl                                0.4.2
keras-tcn                               3.5.0
keras-tuner                             1.4.7
kiwisolver                              1.4.5
kmapper                                 2.0.1
korean-lunar-calendar                   0.3.1
kt-legacy                               1.0.5
langchain                               0.1.12
langchain-community                     0.0.38
langchain-core                          0.1.52
langchain-text-splitters                0.0.2
langcodes                               3.4.0
langsmith                               0.1.67
language_data                           1.2.0
lark                                    1.1.9
lazy_loader                             0.4
lazypredict-nightly                     0.3.0
libclang                                18.1.1
lifelines                               0.28.0
lightgbm                                4.3.0
lightning                               2.1.4
lightning-utilities                     0.11.2
lime                                    0.2.0.1
line-profiler                           4.1.2
linear-operator                         0.5.1
linkify-it-py                           2.0.3
livelossplot                            0.5.5
llama-index                             0.10.19
llama-index-agent-openai                0.1.7
llama-index-cli                         0.1.12
llama-index-core                        0.10.43
llama-index-embeddings-openai           0.1.10
llama-index-indices-managed-llama-cloud 0.1.6
llama-index-legacy                      0.9.48
llama-index-llms-openai                 0.1.22
llama-index-multi-modal-llms-openai     0.1.6
llama-index-program-openai              0.1.6
llama-index-question-gen-openai         0.1.3
llama-index-readers-file                0.1.23
llama-index-readers-llama-parse         0.1.4
llama-parse                             0.4.4
llamaindex-py-client                    0.1.19
llvmlite                                0.42.0
locket                                  1.0.0
logical-unification                     0.4.6
lxml                                    5.1.0
lz4                                     4.3.3
mamba-ssm                               1.2.0.post1
MAPIE                                   0.8.3
marisa-trie                             1.1.1
Markdown                                3.6
markdown-it-py                          3.0.0
marshmallow                             3.21.2
matplotlib                              3.7.5
matplotlib-inline                       0.1.7
mdit-py-plugins                         0.4.1
mdurl                                   0.1.2
mgarch                                  0.3.0
miniKanren                              1.0.3
minorminer                              0.2.13
mistune                                 3.0.2
ml-dtypes                               0.3.2
mlflow                                  2.11.1
mlforecast                              0.10.0
mljar-supervised                        1.1.6
mlxtend                                 0.23.1
mmh3                                    2.5.1
model-index                             0.1.11
modin                                   0.26.1
mplfinance                              0.12.10b0
mpmath                                  1.3.0
msgpack                                 1.0.8
multidict                               6.0.5
multipledispatch                        1.0.0
multiprocess                            0.70.16
multitasking                            0.0.11
murmurhash                              1.0.10
mypy-extensions                         1.0.0
namex                                   0.0.8
nbclient                                0.10.0
nbconvert                               7.16.4
nbformat                                5.10.4
ndindex                                 1.8
nest-asyncio                            1.6.0
networkx                                3.3
neural-tangents                         0.6.5
neuralprophet                           0.8.0
nfoursid                                1.0.1
ngboost                                 0.5.1
ninja                                   1.11.1.1
nlpaug                                  1.1.11
nltk                                    3.8.1
nolds                                   0.5.2
nose                                    1.3.7
notebook                                7.1.3
notebook_shim                           0.2.4
nptyping                                2.4.1
numba                                   0.59.0
numerapi                                2.18.0
numexpr                                 2.10.0
numpy                                   1.26.4
nvidia-cublas-cu12                      12.5.2.13
nvidia-cuda-cupti-cu12                  12.1.105
nvidia-cuda-nvrtc-cu12                  12.1.105
nvidia-cuda-runtime-cu12                12.1.105
nvidia-cudnn-cu12                       8.9.2.26
nvidia-cufft-cu12                       11.0.2.54
nvidia-curand-cu12                      10.3.2.106
nvidia-cusolver-cu12                    11.4.5.107
nvidia-cusparse-cu12                    12.1.0.106
nvidia-ml-py3                           7.352.0
nvidia-nccl-cu12                        2.18.1
nvidia-nvjitlink-cu12                   12.5.40
nvidia-nvtx-cu12                        12.1.105
omegaconf                               2.2.3
onnx                                    1.16.1
onnxruntime                             1.18.0
openai                                  1.30.4
opencensus                              0.11.4
opencensus-context                      0.1.3
opencv-contrib-python-headless          4.9.0.80
opencv-python                           4.10.0.82
opendatalab                             0.0.10
openmim                                 0.3.9
openpyxl                                3.1.2
openxlab                                0.1.0
opt-einsum                              3.3.0
optimum                                 1.18.1
optree                                  0.11.0
optuna                                  3.5.0
ordered-set                             4.1.0
orjson                                  3.10.3
ortools                                 9.9.3963
osqp                                    0.6.7
oss2                                    2.17.0
overrides                               7.7.0
packaging                               24.0
pandas                                  2.1.4
pandas-flavor                           0.6.0
pandas-ta                               0.3.14b0
pandas_market_calendars                 4.4.0
pandocfilters                           1.5.1
panel                                   1.3.8
param                                   2.1.0
parso                                   0.8.4
partd                                   1.4.2
pastel                                  0.2.1
pathos                                  0.3.2
patsy                                   0.5.6
pbr                                     6.0.0
pdf2image                               1.17.0
peewee                                  3.17.3
peft                                    0.11.1
penaltymodel                            1.1.0
PennyLane                               0.35.1
PennyLane-qiskit                        0.35.1
PennyLane_Lightning                     0.35.1
persim                                  0.3.5
pexpect                                 4.9.0
pgmpy                                   0.1.25
pillow                                  10.3.0
pingouin                                0.5.4
plotly                                  5.20.0
plotly-resampler                        0.10.0
plucky                                  0.4.3
pluggy                                  1.5.0
ply                                     3.11
pmdarima                                2.0.4
polars                                  0.20.15
pomegranate                             1.0.4
POT                                     0.9.3
pox                                     0.3.4
ppft                                    1.7.6.8
pprofile                                2.1.0
preshed                                 3.0.9
prompt_toolkit                          3.0.45
prophet                                 1.1.5
proto-plus                              1.23.0
protobuf                                4.25.3
ptyprocess                              0.7.0
PuLP                                    2.8.0
pure-eval                               0.2.2
py-cpuinfo                              9.0.0
py-heat                                 0.0.6
py-heat-magic                           0.0.2
py-lets-be-rational                     1.0.1
py-spy                                  0.3.14
py-vollib                               1.0.1
py4j                                    0.10.9.7
pyaml                                   24.4.0
pyarrow                                 15.0.1
pyarrow-hotfix                          0.6
pyasn1                                  0.6.0
pyasn1_modules                          0.4.0
pybind11                                2.12.0
pycaret                                 3.3.2
pycparser                               2.20
pycryptodome                            3.20.0
pydantic                                2.7.3
pydantic_core                           2.18.4
pydmd                                   1.0.0
pyerfa                                  2.0.1.4
pyfolio-reloaded                        0.9.5
Pygments                                2.18.0
pykalman                                0.9.7
pylev                                   1.4.0
pyluach                                 2.2.0
pymannkendall                           1.4.3
pymc                                    5.10.4
pymdptoolbox                            4.0b3
pynndescent                             0.5.12
pyod                                    2.0.0
Pyomo                                   6.7.1
pyparsing                               3.1.2
pypdf                                   4.2.0
pyportfolioopt                          1.5.5
pyre-extensions                         0.0.30
pyro-api                                0.1.2
pyro-ppl                                1.9.0
pysimdjson                              5.0.2
pyspnego                                0.10.2
pystan                                  3.9.0
pytensor                                2.18.6
pytesseract                             0.3.10
pytest                                  8.2.1
python-dotenv                           1.0.0
python-statemachine                     2.1.2
pytorch-ignite                          0.4.13
pytorch-lightning                       2.1.4
pytorch-metric-learning                 2.3.0
pytorch-tabnet                          4.1.0
pytz                                    2023.4
pyvinecopulib                           0.6.5
pyviz_comms                             3.0.2
PyWavelets                              1.5.0
PyYAML                                  6.0.1
pyzmq                                   26.0.3
qdldl                                   0.1.7.post2
qiskit                                  1.0.2
qiskit-aer                              0.14.2
qiskit-ibm-provider                     0.11.0
qiskit-ibm-runtime                      0.20.0
qtconsole                               5.5.2
QtPy                                    2.4.1
quadprog                                0.1.12
quantconnect-stubs                      16467
quantecon                               0.7.2
QuantLib                                1.33
QuantStats                              0.0.62
querystring-parser                      1.2.4
rauth                                   0.7.3
ray                                     2.10.0
Rbeast                                  0.1.19
rectangle-packer                        2.0.2
regex                                   2024.5.15
requests                                2.28.2
requests-ntlm                           1.2.0
requests-oauthlib                       1.3.1
retrying                                1.3.4
rfc3339-validator                       0.1.4
rfc3986-validator                       0.1.1
rich                                    13.4.2
ripser                                  0.6.8
Riskfolio-Lib                           6.0.0
riskparityportfolio                     0.5.1
river                                   0.21.0
rsa                                     4.9
ruptures                                1.1.9
rustworkx                               0.14.2
s3transfer                              0.10.1
safetensors                             0.4.3
SALib                                   1.5.0
schemdraw                               0.15
scikeras                                0.13.0
scikit-base                             0.7.8
scikit-image                            0.20.0
scikit-learn                            1.4.0
scikit-learn-extra                      0.3.0
scikit-optimize                         0.10.0
scikit-plot                             0.3.7
scikit-tda                              1.0.0
scipy                                   1.11.4
scs                                     3.2.4.post2
sdeint                                  0.3.0
seaborn                                 0.13.2
semantic-version                        2.10.0
Send2Trash                              1.8.3
sentence-transformers                   3.0.0
sentencepiece                           0.2.0
seqeval                                 1.2.2
setuptools-scm                          8.1.0
shap                                    0.45.0
Shimmy                                  1.3.0
simplejson                              3.19.2
simpy                                   4.1.1
sklearn-json                            0.1.0
sktime                                  0.26.0
slicer                                  0.0.7
smart-open                              6.4.0
smmap                                   5.0.1
sniffio                                 1.3.1
sortedcontainers                        2.4.0
soupsieve                               2.5
spacy                                   3.7.4
spacy-legacy                            3.0.12
spacy-loggers                           1.0.5
sqlparse                                0.5.0
srsly                                   2.4.8
stable_baselines3                       2.3.2
stack-data                              0.6.3
stanio                                  0.3.0
statsforecast                           1.4.0
statsmodels                             0.14.1
stevedore                               5.2.0
stochastic                              0.6.0
stockstats                              0.6.2
stopit                                  1.1.2
striprtf                                0.0.26
stumpy                                  1.12.0
symengine                               0.11.0
sympy                                   1.12.1
ta                                      0.11.0
TA-Lib                                  0.4.28
tables                                  3.9.2
tabulate                                0.8.10
tadasets                                0.2.1
tbats                                   1.1.3
tblib                                   3.0.0
tenacity                                8.3.0
tensorboard                             2.16.2
tensorboard-data-server                 0.7.2
tensorboardX                            2.6.2.2
tensorflow                              2.16.1
tensorflow-addons                       0.23.0
tensorflow-io-gcs-filesystem            0.37.0
tensorflow-probability                  0.24.0
tensorflow-text                         2.16.1
tensorflow_decision_forests             1.9.0
tensorly                                0.8.1
tensorrt                                8.6.1.post1
tensorrt-bindings                       8.6.1
tensorrt-libs                           8.6.1
tensortrade                             1.0.3
termcolor                               2.4.0
terminado                               0.18.1
text-unidecode                          1.3
tf2jax                                  0.3.6
tf_keras                                2.16.0
thinc                                   8.2.3
threadpoolctl                           3.5.0
thundergbm                              0.3.17
tifffile                                2024.5.22
tiktoken                                0.7.0
timm                                    0.9.16
tinycss2                                1.3.0
tokenizers                              0.15.2
toml                                    0.10.2
toolz                                   0.12.1
torch                                   2.1.2
torch-cluster                           1.6.3
torch-scatter                           2.1.2
torch-sparse                            0.6.18
torch-spline-conv                       1.2.2
torch_geometric                         2.5.1
torchdata                               0.7.1
torchmetrics                            1.2.1
torchvision                             0.16.2
TPOT                                    0.12.2
tqdm                                    4.65.2
transformers                            4.38.2
triad                                   0.9.6
triton                                  2.1.0
tsdownsample                            0.1.3
tsfresh                                 0.20.2
tslearn                                 0.6.3
tweepy                                  4.14.0
typeguard                               2.13.3
typer                                   0.9.4
typer-config                            1.4.0
types-python-dateutil                   2.9.0.20240316
typing-inspect                          0.9.0
tzdata                                  2024.1
uc-micro-py                             1.0.3
umap-learn                              0.5.5
update-checker                          0.18.0
uri-template                            1.3.0
urllib3                                 1.26.18
utilsforecast                           0.0.10
virtualenv                              20.26.2
wasabi                                  1.1.3
wcwidth                                 0.2.13
weasel                                  0.3.4
webargs                                 8.4.0
webcolors                               1.13
webencodings                            0.5.1
websocket-client                        1.8.0
websockets                              12.0
Werkzeug                                3.0.1
widgetsnbextension                      4.0.11
window_ops                              0.0.15
woodwork                                0.31.0
wordcloud                               1.9.3
wrapt                                   1.16.0
wurlitzer                               3.1.0
x-transformers                          1.30.4
xarray                                  2024.2.0
xarray-einstats                         0.7.0
xgboost                                 2.0.3
xlrd                                    2.0.1
XlsxWriter                              3.2.0
xxhash                                  3.4.1
xyzservices                             2024.4.0
yarl                                    1.9.4
yellowbrick                             1.5
yfinance                                0.2.40
zict                                    3.0.0
zope.event                              5.0
zope.interface                          6.4.post2

Request New Libraries

To request a new library, contact us . We will add the library to the queue for review and deployment. Since the libraries run on our servers, we need to ensure they are secure and won't cause harm. The process of adding new libraries takes 2-4 weeks to complete. View the list of libraries currently under review on the Issues list of the Lean GitHub repository .

Introduction

The VS Code Integrated Development Environment (IDE) lets you work on research notebooks and develop algorithms for backtesting and live trading. When you open a project , the IDE automatically displays. You can access your trading algorithms from anywhere in the world with just an internet connection and a browser.

Supported Languages

The Lean engine supports C# and Python. Python is less verbose, has more third-party libraries, and is more popular among the QuantConnect community than C#. C# is faster than Python and it's easier to contribute to Lean if you have features written in C# modules. Python is also the native language for the research notebooks, so it's easier to use in the Research Environment.

The programming language that you have set on your account determines how autocomplete and IntelliSense are verified and determines the types of files that are included in your new projects. If you have Python set as your programming language, new projects will have .py files. If you have C# set as your programming language, new projects will have .cs files.

Change Languages

To change the default programming language for your new projects, adjust the User: Preferred Language extension setting .

Console

The console panel at the bottom of the IDE provides some helpful information while you're developing algorithms.

Problems

Terminal

The Terminal tab of the panel serves as a command line interface in the directory of your project.

Ask Mia

The Ask Mia tab of the panel is where you can interact with our AI assistant, Mia.

Mia provides contextual assistance to most issues you may encounter when developing a strategy, including build errors, API methods, and best coding practices. It has been trained on hundreds of algorithms and thousands of documentation pages.

To clear the chat with Mia, click the Clear Mia Chat icon in the top-right corner of the panel.

Navigate the File Outline

The Outline section in the Explorer panel is an easy way to navigate your files. The section shows the name of classes, members, and functions defined throughout the file. Click one of the names to jump your cursor to the respective definition in the file. To view the Outline , open a project and then, in the left navigation menu, click the

Explorer icon.

Split the Editor

The editor can split horizontally and vertically to display multiple files at once. Follow these steps to split the editor:

Show and Hide Code Blocks

The editor can hide and show code blocks to make navigating files easier. To hide and show code blocks, open a project and then click the arrow icon next to a line number.

Keyboard Shortcuts

Keyboard shortcuts are combinations of keys that you can issue to manipulate the IDE. They can speed up your workflow because they remove the need for you to reach for your mouse.

To set a key binding for a command, click the pencil icon in the left column of the keyboard shortcuts table, enter the key combination, and then press Enter .

Introduction

Key Features

Requirements

To use a Private Cloud, you need an Institutional Subscription to the Local Platform with the Private-Cloud Add-On. This is licensed per server and supports scaling up to 10 servers. If you're interested in this solution, make an appointment with our team to discuss if it will work for your fund.

Infrastructure Layout

Clients run research notebooks, backtests, optimizations, or live trading strategies on their laptops using the QuantConnect extension in VSCode. The work runs on the centralized servers utilizing the centralized data and powerful servers inside the corporate local network. Results are streamed back to the Local Platform user interface so the quant can research as before.

Deploy a Private Cloud

Presale

This technology has not yet been released and is currently open for discounted presales. If you are interested in using this with your organization, book an appointment to discuss how it could scale an on-premise quant infrastructure.

Introduction

Projects contain files to run backtests, launch research notebooks, perform parameter optimizations, and deploy live trading strategies. You need to create projects in order to create strategies and share your work with other members. Projects enable you to generate institutional-grade reports on the performance of your backtests. You can create your projects from scratch or you can utilize pre-built libraries and third-party packages to expedite your development process.

View All Projects

To view all your projects, open the organization workspace directory on your local machine.

Create Projects

The new project directory is added to your organization workspace directory and the project opens.

Open Projects

Close Projects

Clone Projects

Clone a project to create a new copy of the project and save it within the same organization. When you clone a project, the project files are duplicated but the backtest results and live deployment history are not retained. Cloning enables you to test small changes in your projects before merging the changes back into the original project and start a new live deployment record.

Rename Projects

The project name must only contain

, letters, numbers, and spaces. The project name can't start with a space or be any of the following reserved names: CON, PRN, AUX, NUL, COM1, COM2, COM3, COM4, COM5, COM6, COM7, COM8, COM9, LPT1, LPT2, LPT3, LPT4, LPT5, LPT6, LPT7, LPT8, or LPT9.

Create Project Directories

Set the name of a project to directoryName / projectName to create a project directory.

Set Descriptions

You can give a project a description to provide a high-level overview of the project and its functionality. Descriptions make it easier to return to old projects and understand what is going on at a high level without having to look at the code. The project description is also displayed at the top of backtest reports, which you can create after your backtest completes.

Edit Parameters

Algorithm parameters are hard-coded values for variables in your project that are set outside of the code files. Add parameters to your projects to remove hard-coded values from your code files and to perform parameter optimizations. You can add parameters, set default parameter values, and remove parameters from your projects.

Add Parameters

Set Default Parameter Values

Follow these steps to set the default value of an algorithm parameter in a project:

Delete Parameters

Delete Projects

Encrypt Projects

When you save projects in QuantConnect Cloud, you can save encrypted versions of your project files instead of the raw, human readable, file content. Encrypting your projects gives you an additional layer of protection. To use the encryption system, you provide your own encryption key, which your local browser saves to memory. For more information about project encryption, see Encryption .

Get Project Id

To get the project Id, open the <organizationWorkspace> / <projectName> / config.json file and look for the value of the


    local-id


    cloud-id

key. An example project Id is 13946911.

Introduction

The files in your projects enable you to implement trading algorithms, perform research, and store important information. Python projects start with a main.py and a research.ipynb file. C# projects start with a Main.cs and a Research.ipynb file. Use the main.py or Main.cs file to implement trading algorithms and use the ipynb file to access the Research Environment.

Supported File Types

Code Files

The .py / .cs files are code files. These are the files where you implement your trading algorithm. When you backtest the project or deploy the project to live trading, the LEAN engine executes the algorithm you define in these code files.

Notebook Files

The .ipynb files are notebook files. These are the files you open when you want to access the Research Environment to perform quantitative research.

Custom Report Files

Configuration Files

Projects also contain configuration files, which are .json files, but they aren't displayed in the Explorer panel. These files contain information like the project description, parameters, and shared libraries. For more information about project configuration files, see Configuration .

Result Files

When you run a backtest, optimize some parameters, or deploy a strategy to live trading on your local machine, the results are saved as phyical files in the project directory. Local Platform doesn't push these result files to QuantConnect Cloud.

View Files

To view the files in a project, open the project and then, in the left navigation bar, click the

Explorer icon.

The QC (Workspace) section of the Explorer panel shows the files in the project.

Add Files

Add Directories

You can organize the code and notebook files in your project into directories to make navigating them easier. For example, if you have multiple Alpha models in your strategy, you can create an alphas directory in your project to hold a file for each Alpha model.

Open Files

Close Files

To close a file, at the top of VS Code, click the x button on the file tab you want to close.

To close all of the files in a project, at the top of VS Code, right-click one of the file tabs and then click Close All .

Rename Files and Directories

Delete Files and Directories

Introduction

Project libraries are QuantConnect projects you can merge into your project to avoid duplicating code files. If you have tools that you use across several projects, create a library.

Create Libraries

Add Libraries

Rename Libraries

Remove Libraries

Delete Libraries

Introduction

Version control is the practice of tracking and managing changes to code files. By using version control, you can save an extra back up of your project files in the cloud, keep a history of all code changes, and easily revert changes to your projects.

Create Workspace Repositories

Follow these steps to set up a new version control repository for one of your organization workspaces :

Push Changes to Git

Follow these steps to push the changes of your organization workspace to your version control system:

Introduction

Download Formats

You can download individual data files or bulk download entire universes of assets. For more information about each of these data formats, see Downloading Data .

Physical Location

When you download data from the Dataset Market, it's stored in the data directory in your organization workspace . This is the same directory that LEAN reads data from when you run an algorithm or spin up a research notebook. To change the directory from which LEAN reads the data, open the configuration file and adjust the value under the


    data-folder

key. The data folder should be fast and spacious. Follow these steps to check the approximate size of the datasets in the Dataset Market:

Data Formats

LEAN strives to use an open, human-readable format, so all data is stored in flat files (formatted as CSV or JSON). The data is compressed on disk using zip. For more information about general data formats, see the LEAN Data Formats README in the LEAN GitHub repository. The following README files explain the data formats of specific asset classes:

Live Trading

You need live data providers to inject data into your algorithm so that you can make real-time trading decisions and so that the values of the securities in your portfolio are updated. The data providers you have available to you depend on where you deploy the algorithm. When you request historical data in local algorithms, the historical data comes from your dataset vendor. Familiarize yourself with the quotas and limits of your data provider to avoid errors.

Local Deployments

When you deploy local algorithms, you can use any of the following data providers:

Cloud Deployments

When you deploy algorithms to QuantConnect Cloud, you can use any of the data providers we support in the cloud . Your live algorithms run on our co-located servers that have 10 GB transfer speeds and low latency.

Introduction

Instead of using the data from QuantConnect or your brokerage, you can use Alpha Vantage for historical data. To use Alpha Vantage, you need to get a free or premium API key .

Supported Datasets

Universe Selection

Universe Selection is available with the Alpha Vantage data provider if you download the data from the Dataset Market . The dataset listings show how to download the universe selection data with the CLI. For live trading, you'll need to periodically download the new data from QuantConnect Cloud, which you can automate with Python scripts. For example, the following tutorials explain how to download historical data and download daily updates:

Alternative Data

If you have licensed alternative data with QuantConnect, it works as expected with the Alpha Vantage data provider for research, backtesting, and live trading.

Research

Backtesting

Optimization

Live Trading

The Alpha Vantage data provider is not currently supported for live trading on Local Platform.

Pricing

To view the prices of the Alpha Vantage API packages, see the Premium API Key page on the Alpha Vantage website.

Introduction

Instead of using the live data from your brokerage, you can use data from IEX Cloud. To use IEX Cloud, you need an account subscribed to one or more data bundles . You also must select either the Launch, Grow, or Enterprise subscription tier; this dictates your maximum requests per second. If you don't have an account, register today and get your API key . Pricing information can be found here .

Upon registration, your account will automatically start a 7-day free trial. This trial has access to all API endpoints and a rate limit of 5 requests per second. If you have any questions regarding IEX Cloud's API service, please contact them here: support@iexcloud.io .

The IEX Cloud data provider streams live asset prices from IEX Cloud. If you use this data provider and request historical data, the historical data comes from IEX Cloud.

Supported Datasets

Universe Selection

Universe Selection is available with the IEX Cloud data provider if you download the data from the Dataset Market . The dataset listings show how to download the universe selection data with the CLI. For live trading, you'll need to periodically download the new data from QuantConnect Cloud, which you can automate with Python scripts. For example, the following tutorials explain how to download historical data and download daily updates:

Alternative Data

If you have licensed alternative data with QuantConnect, it works as expected with the IEX Cloud data provider for research, backtesting, and live trading.

Research

Backtesting

Optimization

Live Trading

The following sections explain live trading deployment and data updates when using the IEX Cloud data provider.

Deployment

Follow these steps to deploy a live trading algorithm that uses the IEX Cloud data provider:

Data Updates

If you trade local live algorithms, you'll need to periodically update your US Equity Security Master. Weekly updates are sufficient in most cases.

If you deploy local live algorithms that rely on US Equity universe data, you'll need to download the latest data files every trading day. For an example Python script that updates local US Equity Coarse Universe files, see Download Daily Updates . For an example Python script that updates local US ETF Constituents files, see Download Daily Updates . To update alternative datasets that support universe selection , see the CLI commands in their respective dataset listing.

Pricing

Introduction

Polygon was founded by Quinton Pike in 2017 with the goal to "break down the barriers that have traditionally limited access to high-quality financial data for all". Polygon provides institutional-grade Equity, Option, Index, Forex, and Crypto data for business and educational purposes.

The Polygon data provider streams live asset prices from Polygon. If you use this data provider and request historical data, the historical data comes from Polygon.

Supported Datasets

The Polygon data feed comes directly from Polygon. Our integration supports securities from the following asset classes:

Universe Selection

Universe Selection is available with the Polygon data provider if you download the data from the Dataset Market . The dataset listings show how to download the universe selection data with the CLI. For live trading, you'll need to periodically download the new data from QuantConnect Cloud, which you can automate with Python scripts. For example, the following tutorials explain how to download historical data and download daily updates:

Alternative Data

If you have licensed alternative data with QuantConnect, it works as expected with the Polygon data provider for research, backtesting, and live trading.

Research

Backtesting

Optimization

Live Trading

The following sections explain live trading deployment and data updates when using the Polygon data provider.

Deployment

Follow these steps to deploy a live trading algorithm that uses the Polygon data provider:

Data Updates

If you deploy local live algorithms that trade US Equities or US Equity Options, you'll need to periodically update your US Equity Security Master. Weekly updates are sufficient in most cases.

Pricing

To view the prices of the Polygon API packages, see the Simple Pricing page on the Polygon website.

Introduction

Backtesting is the process of simulating a trading algorithm on historical data. By running a backtest, you can measure how the algorithm would have performed in the past. Although past performance doesn't guarantee future results, an algorithm that has a proven track record can provide investors with more confidence when deploying to live trading than an algorithm that hasn't performed favorably in the past. If you run local backtests, you can leverage your local data and hardware.

Run Backtests

Local Platform provides multiple deployment targets to enable you to run backtests on-premise and in the cloud. To run a backtest, open a project and then click the

Backtest icon. If the project successfully builds, "Received backtest backtestName request" displays. If the backtest successfully launches, the IDE displays the backtest results page in a new tab. If the backtest fails to launch due to coding errors, the new tab displays the error. As the backtest executes, you can close Local Platform and Docker Desktop without interfering with the backtest. Just don't quit Docker Desktop.

View All Backtests

Rename Backtests

We give an arbitrary name (for example, "Smooth Apricot Chicken") to your backtest result files, but you can follow these steps to rename them:

To programmatically set the backtest name, call the


    SetName


    set_name

method.

Results

The backtest results page and the backtests directory in your project show your algorithm's performance. Review the results to see how your algorithm has performed during the backtest and to investigate how you might improve your algorithm before live trading. For more information about backtest results, see Results .

Algorithm Lab Backtests

For information about cloud backtests through the Algorithm Lab, see Getting Started .

Get Backtest Id

To get the backtest Id, see the first line of the log file . An example local backtest Id is 1710698424. An example cloud backtest Id is 8b16cec0c44f75188d82f9eadb310e17.

Introduction

Deploy a backtest to simulate the historical performance of your trading algorithm. Since the same Lean engine is used to run backtests and live trading algorithms, it's easy to transition from backtesting to live trading once you are satisfied with the historical performance of your algorithm. If you find any issues with Lean or our historical data, we'll resolve the issue.

Nodes

A node is a term to describe the compute hardware when your algorithm runs. We create "virtual nodes", which enable you to spin up multiple backtests with your on-premise hardware. When you run multiple backtests, each one runs in a separate container on the same host machine. To view all your virtual nodes, see the Resources panel .

Concurrent Backtesting

Concurrent backtesting is the process of running multiple backtests at the same time. Concurrent backtesting speeds up your strategy development because you don't have to wait while a single backtest finishes executing. You can run as many concurrent backtests as your CPU and RAM will handle.

Build Projects

If the compiler finds errors during the build process, the IDE highlights the lines of code that caused the errors in red. Your projects will automatically build after each keystroke. To manually build a project, open the project and then click the

Build icon.

Run Backtests

Local Platform provides multiple deployment targets to enable you to run backtests on-premise and in the cloud. To run a backtest, open a project and then click the

Stop Backtests

Introduction

View Backtest Results

The backtest results page automatically displays when you deploy a backtest . The backtest results page presents the equity curve, trades, logs, performance statistics, and much more information.

The content in the backtest results page updates as your backtest executes. You can close Local Platform without interrupting the backtest as long as you keep Docker running. If you close the page, to open it again, view all of the project's backtests . Only you can view the results of local backtests. If you run the backtest in QuantConnect Cloud, only you can view its results unless you explicitly make the backtest public. If you delete a backtest result or you are inactive for 12 months, we archive your backtest results.

The information on the backtest results page is also available in its raw form. To access it, see View Result Files .

Runtime Statistics

The banner at the top of the backtest results page displays the runtime statistics of your backtest.

Statistic	Description
Equity	The total portfolio value if all of the holdings were sold at current market rates.
Fees	The total quantity of fees paid for all the transactions.
Holdings	The absolute sum of the items in the portfolio.
Net Profit	The dollar-value return across the entire trading period.
PSR	The probability that the estimated Sharpe ratio of an algorithm is greater than a benchmark (1).
Return	The rate of return across the entire trading period.
Unrealized	The amount of profit a portfolio would capture if it liquidated all open positions and paid the fees for transacting and crossing the spread.
Volume	The total value of assets traded for all of an algorithm's transactions.

To view the runtime statistics data in JSON format, open the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / <algorithmId>.json file and search for the


    RuntimeStatistics

key.

Built-in Charts

The backtest results page displays a set of built-in charts to help you analyze the performance of your algorithm. The following table describes the charts displayed on the page:

Chart	Description
Strategy Equity	A time series of equity and periodic returns.
Capacity	A time series of strategy capacity snapshots.
Drawdown	A time series of equity peak-to-trough value.
Benchmark	A time series of the benchmark closing price (SPY, by default).
Exposure	A time series of long and short exposure ratios.
Assets Sales Volume	A chart showing the proportion of total volume for each traded security.
Portfolio Turnover	A time series of the portfolio turnover rate.
Portfolio Margin	A stacked area chart of the portfolio margin usage. For more information about this chart, see Portfolio Margin Plots .

To view the chart data in JSON format, open the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / <algorithmId>.json file and search for the


    Charts

key.

Custom Charts

Supported Chart Types

If you use


    SeriesType.Candle

and plot enough values, the plot displays candlesticks. However, the


    Plot


    plot

method only accepts one numerical value per time step, so you can't plot candles that represent the open, high, low, and close values of each bar in your algorithm. The charting software automatically groups the data points you provide to create the candlesticks, so you can't control the period of time that each candlestick represents.

Supported Markers

When you create scatter plots, you can set a marker symbol. We support the following marker symbols:

Chart Quotas

If you execute backtests in QuantConnect Cloud, see Custom Charts for more information about the charting quotas.

If you execute local backtests, the charting quotas are set by the


    maximum-chart-series

and


    maximum-data-points-per-chart-series

configuration settings .

Demonstration

Adjust Charts

Toggle Charts

To display and hide a chart on the backtest results page, in the Select Chart section, click the name of a chart.

Toggle Chart Series

To display and hide a series on a chart on the backtest results page, click the name of a series at the top of a chart.

Adjust the Display Period

To zoom in and out of a time series chart on the backtest results page, perform either of the following actions:

After you zoom in on a chart, slide the horizontal bar at the bottom of the chart to adjust the time frame that displays.

Resize Charts

To resize a chart on the backtest results page, hover over the bottom-right corner of the chart. When the resize cursor appears, hold the left mouse button and then drag to the desired size.

Move Charts

To move a chart on the backtest results page, click, hold, and drag the chart title.

Refresh Charts

Refreshing the charts on the backtest results page resets the zoom level on all the charts. If you refresh the charts while your algorithm is executing, only the data that was seen by the Lean engine after you refreshed the charts is displayed. To refresh the charts, in the Select Chart section, click the reset icon.

Key Statistics

The backtest results page displays many key statistics to help you analyze the performance of your algorithm.

Overall Statistics

The Overview tab on the backtest results page displays tables for Overall Statistics and Rolling Statistics. The Overall Statistics table displays the following statistics:

Some of the preceding statistics are sampled throughout the backtest to produce a time series of rolling statistics. The time series are displayed in the Rolling Statistics table.

To view the data from the Overall Statistics and Rolling Statistics tables in JSON format, open the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / <algorithmId>.json file.

Ranking

If you run a cloud backtest, the backtest results page displays a Ranking section that shows the PSR and rank (percentile) of your algorithm relative to other algorithms in QuantConnect Cloud. For more information about this section, see Key Statistics .

Research Guide

Reports

Backtest reports provide a summary of your algorithm's performance during the backtest period. Follow these steps to generate one:

If you create a report for a local backtest, the report is stored in the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> directory as report.html and report.pdf .

Customize the Report HTML

The Report / template.html file in the LEAN GitHub repository defines the stucture of the reports you generate. To override the HTML file, add a report.html file to your project . To include some of the information and charts that are in the default report, use the report keys in the Report / ReportKey.cs file in the LEAN GitHub repository. For example, to add the Sharpe ratio of your backtest to the custom HTML file, use


    {{$KPI-SHARPE}}

To include the crisis event plots in your report, add the


    {{$HTML-CRISIS-PLOTS}}

key and then define the structure of the individual plots inside of


    <!--crisis

and


    crisis-->

. Inside of this comment, you can utilize the


    {{$TEXT-CRISIS-TITLE}}

and


    {{$PLOT-CRISIS-CONTENT}}

keys. For example, the following HTML is the default format for each crisis plot:

To include the algorithm parameters in your report, add the


    {{$PARAMETERS}}

key and then define the HTML element inside of


    <!--parameters

and


    parameters-->

. Inside of this comment, you can use special keys


    {{$KEY<parameterIndex>}}

and


    {{$VALUE<parameterIndex>}}

, which represent the key and value of a single parameter. For example, the following HTML is the default format for the parameters element:

In the preceding example,


    {{$KEY0}}

is the name of the first parameter in the algorithm and


    {{$VALUE0}}

is its value.

Customize the Report CSS

Orders

The backtest results page displays the orders of your algorithm and you can view them on your local machine.

View in the GUI

To see the orders that your algorithm created, open the backtest results page and then click the Orders tab. If there are more than 10 orders, use the pagination tools at the bottom of the Orders Summary table to see all of the orders. Click on an individual order in the Orders Summary table to reveal all of the order events , which include:

Access the Order Summary CSV

To view the orders data in CSV format, open the backtest results page, click the Orders tab, and then click Download Orders . The content of the CSV file is the content displayed in the Orders Summary table when the table rows are collapsed. The timestamps in the CSV file are based in Coordinated Universal Time (UTC). If you download the order summary CSV for a local backtest, the file is stored in <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / orders.csv .

Access the Orders JSON

To view all of the content in the Orders Summary table, open the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / <algorithmId>.json file and search for the


    Orders

key.

Access the Order Events JSON

To view all of the order events for a local backtest, open the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / <algorithmId>-order-events.json file.

Access in Jupyter Notebooks

Insights

The backtest results page displays the insights of your algorithm and you can view the raw insight data on your local machine.

View in the GUI

To see the insights your algorithm emit, open the backtest result page and then click the Insights tab. If there are more than 10 insights, use the pagination tools at the bottom of the Insights Summary table to see all of the insights. The timestamps in the Insights Summary table are based in Eastern Time (ET).

Open Raw JSON

To view the insights in JSON format, open the backtest result page, click the Insights tab, and then click Download Insights . The timestamps in the CSV file are based in Coordinated Universal Time (UTC).

If you run a local backtest, the JSON file is also available in the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / <algorithmId>-alpha-insights.json file.

Logs

The backtest results page displays the logs of your backtest and you can view them on your local machine. The timestamps of the statements in the log file are based in your algorithm time zone .

View in the GUI

To see the log file that was created throughout a backtest, open the backtest result page and then click the Logs tab.

Download Log Files

To download the log file that was created throughout a backtest, follow these steps:

If you ran a local backtest, the log file is automatically saved on your local machine when the backtest completes.

Access Local Log Files

To view the log file of a local backtest, open the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / <algorithmId>-log.txt file.

Project Files

The backtest results page displays the project files used to run the backtest. To view the files, click the Code tab. By default, the main.py Main.cs file displays. To view other files in the project, click the file name and then select a different file from the drop-down menu.

If you ran a local backtest, the project files are also available in the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> / code directory.

View Result Files

To view the results files of local backtests, run a local backtest and then open the <organizationWorkspace> / <projectName> / backtests / <unixTimestamp> directory. The following table describes the initial contents of the backtest result directories:

The backtest result directories can contain the following additional files if you request them:

View All Backtests

Rename Backtests

We give an arbitrary name (for example, "Smooth Apricot Chicken") to your backtest result files, but you can follow these steps to rename them:

File/Directory	Description
code /	A directory containing a copy of the files that were in the project when you ran the backtest.
<backtestId>-alpha-results.json Ex: 1967791529-alpha-results.json	A file containing all of the backtest insights . This file only exists if you emit insights during the backtest.
<backtestId>-log.txt Ex: 1967791529-log.txt	A file containing all of the backtest logs .
<backtestId>-order-events.json Ex: 1967791529-order-events.json	A file containing all of the backtest order events .
<backtestId>.json Ex: 1967791529.json	A file containing the following data: Runtime statistics Charts The data in the Overview tab The data in the Orders tab The algorithm configuration settings
config	A file containing some configuration settings, including the backtest Id, Docker container name, and backtest name.
data-monitor-report-<backtestDate><unixTimestamp>.json Ex: data-monitor-report-20230614155459950.json	A file containing statistics on the algorithm's data requests.
failed-data-requests-<backtestDate><unixTimestamp>.txt Ex: failed-data-requests-20230614155451004.txt	A file containing all the local data paths that LEAN failed to load during the backtest.
log.txt	A file containing the syslog.
succeeded-data-requests-<backtestDate><unixTimestamp>.txt Ex: succeeded-data-requests-20230614155451004.txt	A file containing all the local data paths that LEAN successfully loaded during the backtest.

File	Description	Request Procedure
orders.csv	A file containing all of the data from the Orders table when the table rows are collapsed.	See Orders
report.html and report.pdf	A file containing the backtest report	See Reports

To programmatically set the backtest name, call the


    SetName


    set_name

method.

Clone Backtests

Hover over the backtest you want to clone, and then click the clone icon that appears to clone the backtest.

Delete Backtests

Hover over the backtest you want to delete, and then click the trash can icon that appears to delete the backtest.

Introduction

The debugger is a built-in tool to help you debug coding errors while backtesting. The debugger enables you to slow down the code execution, step through the program line-by-line, and inspect the variables to understand the internal state of the program.

Requirements

Breakpoints

Breakpoints are lines in your algorithm where execution pauses. You need at least one breakpoint in your code files to start the debugger. Open a project to start adjusting its breakpoints.

Add Breakpoints

Edit Breakpoint Conditions

Enable and Disable Breakpoints

Remove Breakpoints

Launch Debugger

If the Run and Debug panel is not open, it opens when the first breakpoint is hit.

Control Debugger

Inspect Variables

Option	Additional Steps	Description
Expression	Enter an expression and then press Enter .	The breakpoint only pauses the algorithm when the expression is true.
Hit Count	Enter an integer and then press Enter .	The breakpoint doesn't pause the algorithm until its hit the number of times you specify.

Name	Default Keyboard Shortcut	Description
Continue		Continue execution until the next breakpoint
Step Over	Alt+F10	Step to the next line of code in the current or parent scope
Step Into	Alt+F11	Step into the definition of the function call on the current line
Restart	Shift+F11	Restart the debugger
Disconnect	Shift+F5	Exit the debugger

After you launch the debugger, you can inspect the state of your algorithm as it executes each line of code. You can inspect local variables or custom expressions. The values of variables in your algorithm are formatted in the IDE to improve readability. For example, if you inspect a variable that references a DataFrame, the debugger represents the variable value as the following:

Local Variables

The Variables section of the Run and Debug panel shows the local variables at the current breakpoint. If a variable in the panel is an object, click it to see its members. The panel updates as the algorithm runs.

Custom Expressions

The Watch section of the Run and Debug panel shows any custom expressions you add. For example, you can add an expression to show the current date in the backtest.

Introduction

Speed Issues

If you notice that backtests run faster in QuantConnect Cloud than on-premise, it's likely because we host special hardware that's optimized for LEAN. For more information about our hardware, see Backtesting Nodes .

Local and Cloud Result Differences

If your algorithm produces different results when you backtest it in QuantConnect Cloud versus on-premise, it's usually because of differences in data. For example, if you don't have the latest version of the US Equity Security Master , you will likely be missing some splits and dividends, which impact the historical prices of adjusted data. In this case, to avoid differences in the backtest results, update your local copy of the US Equity Security Master every day. For more information about downloading data from the Dataset Market so you have the same data on-premise as in QuantConnect Cloud, see Downloading Data .

Introduction

Before you run backtests, we recommend testing your hypothesis in the Research Environment. It's easier to perform data analysis and produce plots in the Research Environment than in a backtest.

Before backtesting or live trading with machine learning models, you may find it beneficial to train them in the Research Environment, save them in the Object Store, and then load them from the Object Store into the backtesting and live trading environment

In the Research Environment, you can also use the QuantConnect API to import your backtest results for further analysis.

Example

The following snippet demonstrates how to use the Research Environment to plot the price and Bollinger Bands of the S&P 500 index ETF, SPY:

The following snippet demonstrates how to use the Research Environment to print the price of the S&P 500 index ETF, SPY:

Open Notebooks

Local Platform provides multiple deployment targets to enable you to open notebooks on-premise and in QuantConnect Cloud. Each new project you create contains a notebook file by default. Follow these steps to open it:

Run Notebook Cells

Notebooks are a collection of cells where you can write code snippets or MarkDown. To execute a cell, press Shift+Enter .

The following describes some helpful keyboard shortcuts to speed up your research:

Stop Nodes

Add Notebooks

Rename Notebooks

Delete Notebooks

Learn Jupyter

Introduction

Keyboard Shortcut	Description
Shift+Enter	Run the selected cell.
a	Insert a cell above the selected cell.
b	Insert a cell below the selected cell.
x	Cut the selected cell.
v	Paste the copied or cut cell.
z	Undo cell actions.

Type	Name	Producer
Text	Jupyter Tutorial	tutorialspoint
Text	Jupyter Notebook Tutorial: The Definitive Guide	DataCamp
Text	An Introduction to DataFrame	Microsoft Developer Blogs

This page is an introduction to the Research Environment for the Local Platform Lab. For more comprehensive information on using research notebooks, see the Research Environment documentation product.

Nodes

A node is a term to describe the compute hardware when your notebook runs. We create "virtual nodes", which enable you to spin up multiple research notebook with your on-premise hardware. When you run multiple notebooks, each one runs in a separate container on the same host machine. To view all your virtual nodes, see the Resources panel .

Concurrent Research

Concurrent research is the process of running multiple notebooks at the same time. Concurrent research speeds up your research process because you don't have to wait while a cell from a notebook finishes executing. You can run as many concurrent local notebooks as your CPU and RAM can handle.

Deployment Targets

Local Platform provides multiple deployment targets to enable you to open notebooks on-premise and in QuantConnect Cloud. When you open a notebook, it uses the hardware and data that's available on the deployment target machine.

Select Kernel

When you open a notebook , it automatically tries to connect to the correct Jupyter server and select the correct kernel. If it doesn't correctly select the kernel, the top-right corner of the notebook displays a Select Kernel button and the notebook won't let you run any of the cells. If this occurs, follow these steps to fix the issue:

Save Notebooks

Introduction

Parameter optimization is the process of finding the optimal algorithm parameters to maximize or minimize an objective function. For instance, you can optimize your indicator parameters to maximize the Sharpe ratio that your algorithm achieves over a backtest. Optimization can help you adjust your strategy to achieve better backtesting performance, but be wary of overfitting. If you select parameter values that model the past too closely, your algorithm may not be robust enough to perform well using out-of-sample data.

Launch Optimization Jobs

Local Platform provides multiple deployment targets to enable you to run backtests on-premise and in QuantConnect Cloud.

View Individual Backtest Results

The optimization results page displays a Backtests table that includes all of the backtests that ran during the optimization job. The table lists the parameter values of the backtests in the optimization job and their resulting values for the objectives.

Open the Backtest Results Page

To open the backtest result page of one of the backtests in the optimization job, click a backtest in the table.

Download the Table

To download the table, right-click one of the rows, and then click .

Filter the Table

Toggle Table Columns

Sort the Table Columns

In the Backtests table, click one of the column names to sort the table by that column.

View All Optimizations

Rename Optimizations

We give an arbitrary name (for example, "Smooth Apricot Chicken") to your optimization result files, but you can follow these steps to rename them:

Delete Optimizations

Hover over the optimization you want to delete and then click the trash can icon that appears to delete the optimization result.

Result Files

To view the results files of a local optimization job, open the <organizationWorkspace> / <projectName> / optimizations / <optimizationName> directory. The following table describes the initial contents of the optimization result directories:

Algorithm Lab Optimizations

File/Directory	Description
code /	A directory containing a copy of the files that were in the project when you ran the optimization.
<backtestId> / Ex: 1c5b8eff-89c0-432f-98c7-60b73265b188 /	A directory containing the backtest result files for one of the backtest in the optimization job. There is separate directory for each backtest in the optimization job.
log.txt	A file containing the syslog.
config	A file containing some configuration settings, including the optimization Id, Docker container name, and optimization name.
optimizer-config.json	A file containing some configuration settings, including the parameters, strategy, and constrains.
optimization-result-<optimizationId>.json Ex: optimization-result-2455523408.json	A file containing additional results, including runtime statistics.

For information about cloud optimizations through the Algorithm Lab, see Getting Started .

Get Optimization Id

To get the optimization Id, open the optimization result page and then scroll down to the table that shows the individual backtest results . The optimization Id is at the top of the table. An example local optimization Id is 2726379566. An example cloud optimization Id is O-696d861d6dbbed45a8442659bd24e59f.

Introduction

A live algorithm is an algorithm that trades in real-time with real market data. Local Platform provides multiple deployment targets to enable you to run live algorithms on-premise and in QuantConnect Cloud. If you run live algorithms on-premise, you are prone to downtime from power outages, computer crashes, and natural disasters. If you don't want to be at risk to these, run your algorithms on QuantConnect Cloud

Deploy Live Algorithms

Follow these steps to deploy a live trading algorithm with the Interactive Brokers (IB) brokerage:

The deployment process can take up to 5 minutes. When the algorithm deploys, the live results page displays. If you know your brokerage positions before you deployed, you can verify they have been loaded properly by checking your equity value in the runtime statistics, your cashbook holdings, and your position holdings.

Stop Live Algorithms

The live trading results page has a Stop button to immediately stop your algorithm from executing. When you stop a live algorithm, your portfolio holdings are retained. Stop your algorithm if you want to perform any of the following actions:

Furthermore, if you receive new securities in your portfolio because of a reverse merger, you also need to stop and redeploy the algorithm.

LEAN actively terminates live algorithms when it detects interference outside of the algorithm's control to avoid conflicting race conditions between the owner of the account and the algorithm, so avoid manipulating your brokerage account and placing manual orders on your brokerage account while your algorithm is running. If you need to adjust your brokerage account holdings, stop the algorithm, manually place your trades, and then redeploy the algorithm.

Liquidate Live Algorithms

The live results page has a Liquidate button that acts as a "kill switch" to sell all of your portfolio holdings. If your algorithm has a bug in it that caused it to purchase a lot of securities that you didn't want, this button let's you easily liquidate your portfolio instead of placing many manual trades. When you click the Liquidate button, if the market is open for an asset you hold, the algorithm liquidates it with market orders. If the market is not open, the algorithm places market on open orders. After the algorithm submits the liquidation orders, it stops executing.

Data Providers

Result Files

When you deploy a live algorithm, the live results page automatically displays. To view the results in their raw form, open the <organizationWorkspace> / <projectName> / live / <timeStamp> directory. The following table describes the initial contents of the live result directories:

Algorithm Lab Live Algorithms

File/Directory	Description
code /	A directory containing a copy of the files that were in the project when you deployed the algorithm.
L-<deploymentId>.json Ex: L-3712451018.json	A file containing the following data: Holdings and cash Account currency Charts Orders Runtime statistics Server statistics
L-<deploymentId>-<date>_minute.json Ex: L-3712451018-2023-06-22_minute.json	A file similiar to the L-<deploymentId>.json file, but the values of the chart data are only sampled every 10 minutes.
L-<deploymentId>-<date>_10minute.json Ex: L-3712451018-2023-06-22_10minute.json	A file similiar to the L-<deploymentId>.json file, but the values of the chart data are only sampled every 10 minutes.
L-<deploymentId>-<date>_second_Strategy%20Equity.json Ex: L-3712451018-2023-06-22-19_second_Strategy%20Equity.json	A file containing the algorithm holdings, chart, and orders. The values of the chart data are sampled every few seconds.
L-<deploymentId>-log.txt Ex: L-3712451018	A file containing all of the live trading logs.
log.txt	A file containing the syslog.
config	A file containing some configuration settings, including the deployment Id, brokerage name, and Docker container name.

For information about live trading in the cloud through the Algorithm Lab, see Getting Started .

Get Deployment Id

To get the live deployment Id, open the log file and enter "Launching analysis for" into the search bar. The log file shows all of the live deployment Ids for the project. An example local deployment Id is L-3554110262. An example cloud deployment Id is L-6bf91128391608d0728ff90b81bfca41. If you have deployed the project multiple times, use the most recent deployment Id in the log file.

Introduction

The Object Store is an organization-specific key-value storage location to save and retrieve data. Similar to a dictionary or hash table, a key-value store is a storage system that saves and retrieves objects by using keys. A key is a unique string that is associated with a single record in the key-value store and a value is an object being stored. Some common use cases of the Object Store include the following:

The Object Store is shared across the entire organization. Using the same key, you can access data across all projects in an organization.

Supported Types

The Object Store has helper methods to store strings, JSON objects, XML objects, and bytes.

To store an object that is in a different format, you need to encode it to one of the supported data types. For instance, if you train a machine learning model and it is in binary format, encode it into base 64 before saving it.

Storage Location

The Object Store is organization-specific. When you save data in the Object Store , it creates a new file in the <organizationWorkspace> / storage / <projectName> directory and names the file with the key you provide. To access the storage data of project A from project B, include the project id of project A to the key.

Research to Live Considerations

When you deploy a live algorithm, you can access the data within minutes of modifying the Object Store. Ensure your algorithm is able to handle a changing dataset.

Delete Storage

To free up storage space, delete the key-value pairs in the Object Store by calling the


    Delete


    delete

method with a key.

Alternatively, delete the files in <organizationWorkspace> / <projectName> / storage directory.

Feature	Platform
Feature	Local Platform	Cloud Platform
Development Environment _{The tool you can use to edit project files}	Any IDE	Cloud-hosted VS Code
Version Control _{Track file changes over time and easily revert mistakes}	Your own git systems	Access historical project files through your backtest results
Anonymous Projects _{Create and edit local projects without syncing to QuantConnect Cloud}	Self-sovereign security	Managed by QuantConnect
Custom LEAN Versions _{Build and run custom versions of LEAN}
Autocomplete _{Easy-to-use tool to speed up your development}

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LOCAL PLATFORM

Quant Research On-Premise

Securely deploy quantitative strategies on-premise with proprietary datasets.

Table of Content

Key Concepts

Key Concepts

Getting Started

QUANTCONNECT LOCAL PLATFORM

Follow these steps to create a new trading algorithm and backtest it in QuantConnect Cloud:

Key Concepts

Features

Introduction

Hybrid Workflow

Version Control

Self-Sovereign Security

Custom LEAN Images

On-Premise Compute

Private Cloud

Coding

Backtesting & Optimization

Live Trading

Key Concepts

Deployment Targets

Introduction

Local

Cloud

Private Cloud

Comparison

Data

Compute

Management

Hardware Procurement

Installation

Install on Windows

Install on macOS

Install on Linux

See Also

Installation

Install on Windows

Introduction

Requirements

Install Docker

Install Local Platform

Next Steps

Troubleshooting

Docker with WSL 2 Features

Windows Security

Docker Not Found

LEAN CLI Account Syncronization

Further Support

Installation

Install on macOS

Introduction

Requirements

Install Docker

Install Local Platform

Next Steps

Troubleshooting

Docker Not Found

LEAN CLI Account Syncronization

Further Support

Installation

Install on Linux

Introduction

Requirements

Install Docker

Install Local Platform

Next Steps

Troubleshooting

Docker Not Found

LEAN CLI Account Syncronization

Further Support

Development Environment

Development Environment

Authentication

Introduction

Log In

Log Out

Troubleshooting

Learn to use
QuantConnect
and Explore
Features

Quant Research
On-Premise

Securely deploy quantitative
strategies on-premise with
proprietary datasets.